Automated diagnostic system and method including alternative symptoms

ABSTRACT

Structure-based processing includes a method of diagnosing diseases that works by arranging diseases, symptoms, and questions into a set of related disease, symptom, and question structures, such as objects or lists, in such a way that the structures can be processed to generate a dialogue with a patient. A structure-based processing system organizes medical knowledge into formal structures and then executes those structures on a structure engine to automatically select the next question. Patient responses to the questions lead to more questions and ultimately to a diagnosis. An object-oriented embodiment includes software objects utilized as active, intelligent agents where each object performs its own tasks and calls upon other objects to perform their tasks at the appropriate time to arrive at a diagnosis. Alternative symptoms, synergies, encoding of patient responses, multiple diagnostic modes, disease profiles or timelines, and the reuse of diagnostic objects enhance the processing of the system and method.

PRIORITY

[0001] The benefit under 35 U.S.C. §119(e) of U.S. provisionalapplication Application No. 60/182,176, filed Feb. 14, 2000, entitled“AUTOMATED DIAGNOSTIC SYSTEM AND METHOD” is hereby claimed.

RELATED APPLICATIONS

[0002] The subject matter of U.S. patent applications: application Ser.No. ______, filed Feb. 14, 2001 and entitled “AUTOMATED DIAGNOSTICSYSTEM AND METHOD INCLUDING SYNERGIES”; application Ser. No. ______,filed Feb. 14, 2001 and entitled “AUTOMATED DIAGNOSTIC SYSTEM AND METHODINCLUDING ENCODING PATIENT DATA”; application Ser. No. ______, filedFeb. 14, 2001 and entitled “AUTOMATED DIAGNOSTIC SYSTEM AND METHODINCLUDING MULTIPLE DIAGNOSTIC MODES”; application Ser. No. ______, filedFeb. 14, 2001 and entitled “AUTOMATED DIAGNOSTIC SYSTEM AND METHODINCLUDING DISEASE TIMELINE”; and application Ser. No. ______, filed Feb.14, 2001 and entitled “AUTOMATED DIAGNOSTIC SYSTEM AND METHOD INCLUDINGREUSE OF DIAGNOSTIC OBJECTS” are related to this application.

BACKGROUND OF THE INVENTION

[0003] 1. Field of the Invention

[0004] The field of the invention relates to computerized medicaldiagnostic systems. More particularly, embodiments of the presentinvention relate to a computerized system for time-based diagnosis of apatient's medical complaint by use of dynamic data structures.

[0005] 2. Description of the Related Technology

[0006] Health care costs currently represent a significant portion ofthe United States Gross National Product and are generally rising fasterthan any other component of the Consumer Price Index. Moreover, usuallybecause of an inability to pay for medical services, many people aredeprived of access to even the most basic medical care and information.

[0007] Many people delay in obtaining, or are prevented from seeking,medical attention because of cost, time constraints, or inconvenience.If the public had universal, unrestricted, and easy access to medicalinformation, many diseases could be prevented. Likewise, the earlydetection and treatment of numerous diseases could keep many patientsfrom reaching the advanced stages of illness, the treatment of which isa significant part of the financial burden attributed to our nation'shealth care system. It is clear that the United States is facinghealth-related issues of enormous proportions and that present solutionsare not robust.

[0008] Previous attempts at tackling the healthcare problem haveinvolved various forms of automation. Some of these attempts have beenin the form of a dial-in library of answers to medical questions. Otherattempts have targeted providing doctors with computerized aids for useduring a patient examination. These methods involve static procedures oralgorithms. What is desired is an automated way of providing to apatient medical advice and diagnosis that is quick, efficient andaccurate. Such a medical advice system should be modular to allowexpansion for new types of medical problems or methods of detection.

SUMMARY OF THE INVENTION

[0009] Structure-based processing is a method of diagnosing diseasesthat works by arranging diseases, symptoms, and questions into a set ofrelated disease, symptom, and question structures, such as objects orlists, in such a way that the structures can be processed to generate adialogue with a patient. Each question to the patient generates one of aset of defined responses, and each response generates one of a set ofdefined questions. This establishes a dialogue that elicits symptomsfrom the patient. The symptoms are processed and weighted to rulediseases in or out. The set of ruled-in diseases establishes thediagnosis. A structure-based processing system organizes medicalknowledge into formal structures and then executes those structures on astructure engine, such as a list-based engine, to automatically selectthe next question. The responses to the questions lead to more questionsand ultimately to a diagnosis.

[0010] One aspect of the invention includes a data schema for diagnosinga disease, comprising a first disease object associated with a set offirst disease symptom objects, at least one first disease symptom objecthaving an actual symptom weight, and a second disease object associatedwith a set of second disease symptom objects, at least one seconddisease symptom object corresponding to the at least one first diseasesymptom object and having an alternative symptom weight.

[0011] An additional aspect of the invention includes a method ofautomated medical diagnosis of a patient, comprising providing at leasta first symptom element having a first symptom weight, retrieving analternative weight for the first symptom, and applying the retrievedalternative weight to a diagnostic score so as to diagnose a medicalcondition.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012]FIG. 1 is a flowchart of one embodiment of a diagnostic loopperformed by a structure-based engine of a medical diagnostic andtreatment advice system.

[0013]FIG. 2 is a flowchart of the Set Up Diagnostic Loop function shownin FIG. 1.

[0014]FIG. 3 is a flowchart of the Set Up Disease-Symptom Structurefunction shown in FIG. 2.

[0015]FIG. 4 is a flowchart of the Pick Current Disease function shownin FIG. 1.

[0016]FIG. 5 is a flowchart of the Pick Current Symptom function shownin FIG. 1.

[0017]FIG. 6 is a flowchart of the Obtain Symptom Value function shownin FIG. 1.

[0018]FIG. 7 is a flowchart of the Use Question Valuator Object functionshown in FIG. 6.

[0019]FIG. 8 is a flowchart of the Use Formula Valuator Object functionshown in FIG. 6.

[0020]FIG. 9 is a flowchart of the Use Lookup Valuator Object functionshown in FIG. 6.

[0021]FIG. 10 is a flowchart of the Use Spectrum of Terms ValuatorObject function shown in FIG. 6.

[0022]FIG. 11 is a flowchart of the Apply Symptom Value function shownin FIG. 1.

[0023]FIG. 12 is a flowchart of the Compute Synergies function shown inFIG. 11.

[0024]FIG. 13 is a flowchart of the Calculate FSS Synergy function shownin FIG. 12.

[0025]FIG. 14 is a flowchart of the Calculate Onset [Offset] Synergyfunction shown in FIG. 12.

[0026]FIG. 15 is a flowchart of the Analyze Onset [Offset] Synergyfunction shown in FIG. 14.

[0027]FIG. 16 is a flowchart of the Compute Onset [Offset] Slopefunction shown in FIG. 15.

[0028]FIG. 17 is a flowchart of the Compute Onset [Offset] Trendfunction shown in FIG. 15.

[0029]FIG. 18 is a flowchart of the Calculate Sequencing Synergyfunction shown in FIG. 12.

[0030]FIG. 19 is a flowchart of the Calculate Simultaneous Synergyfunction shown in FIG. 12.

[0031]FIG. 20 is a flowchart of the Calculate Time Profile Synergyfunction shown in FIG. 12.

[0032]FIG. 21 is a flowchart of the Update and Record function shown inFIG. 1.

[0033]FIG. 22 is a flowchart of the Review Diagnoses function shown inFIG. 1.

[0034]FIG. 23 is a flowchart of the Review Diagnostic Goals functionshown in FIG. 22.

[0035]FIG. 24 is a flowchart of the Shut Down Diagnostic Loop functionshown in FIG. 1.

[0036]FIG. 25 is a flowchart of the Symptom Slope function shown inFIGS. 16 and 26.

[0037]FIG. 26 is a flowchart of the Symptom Trend function shown in FIG.17.

[0038]FIG. 27 is a diagram of an exemplary disease-symptom matrix.

[0039]FIG. 28 is a diagram of an exemplary generic disease timeline.

[0040]FIG. 29a is a diagram of one configuration of objects used by anembodiment of the medical diagnostic and treatment advice system.

[0041]FIG. 29b is a diagram of an exemplary on-line use of theconfiguration of objects shown in FIG. 29a to develop a diagnosis.

[0042]FIG. 30 is a flowchart of an alternative symptom weights featureof the medical diagnostic and treatment advice system.

[0043]FIG. 31 is a flowchart of a reuse of medical objects feature ofthe medical diagnostic and treatment advice system.

[0044]FIG. 32a is a flowchart of a setup of symptom elements aspect ofthe medical diagnostic and treatment advice system.

[0045]FIG. 32b is a flowchart of a mode switching feature using thesymptom elements of FIG. 32a.

[0046]FIG. 33 is a flowchart of one embodiment of a disease timelinesaspect of the medical diagnostic and treatment advice system.

[0047]FIG. 34 is a flowchart of another embodiment of a diseasetimelines aspect of the medical diagnostic and treatment advice system.

[0048]FIG. 35 is a block diagram illustrating components of oneembodiment of the computerized medical diagnostic and treatment advice(MDATA) system of the present invention.

DETAILED DESCRIPTION

[0049] The following detailed description presents a description ofcertain specific embodiments of the present invention. However, thepresent invention may be embodied in a multitude of different ways asdefined and covered by the claims. In this description, reference ismade to the drawings wherein like parts are designated with likenumerals throughout.

[0050] The detailed description will begin with an overview of thespecific embodiments followed by a description of each of the drawings.The overview is partitioned into the following sections: Terminology,Object-based Medical Diagnosis, Object-based Method, Disease Object,Symptom Object, Valuator Object, Question Object, Node Object,List-based Engine Concepts, Dynamic Rules and Goals, Dynamic Momentum,Horizontal Axis of Inquiry (HAI), Vertical Axis of Inquiry (VAI),Alternative Symptoms, Disease Timeline, Spectrum of Terms/PQRST Code,and Synergy.

I. TERMINOLOGY

[0051] The terms presented in this section include text to assist inunderstanding their meanings. Nonetheless, nothing in this section ismeant to limit the meanings attributed to each word.

[0052] Patient

[0053] At some point in everyone's life, there are situations thatcreate a real health problem, one that requires medical attention andtreatment. The health problem may, for example, be caused externally, byslipping on a bar of soap in the shower, being scratched by a cat,lifting a child in an awkward manner, inhaling some airborne bacteria,being stung by a mosquito that carries malaria, or getting into atraffic accident. Or the problem may arise internally, by some tiny tubeclogging up, some organ being overloaded, some tissue degenerating withage, some anatomic system getting too much of one chemical and notenough of another, or some cells deciding to grow unchecked into a cystor a tumor, for instance. The person with the health problem is calledthe patient, in order to distinguish him or her from other personsinvolved in the case, such as the patient's friends and relatives,doctors and nurses, therapists and pharmacists, lawyers and insuranceagents and HMOs.

[0054] Disease

[0055] There are many words for the general concept of someone beingsick. A patient may be said to have an abnormality, affliction, ailment,anomaly, cause, complaint, condition, disease, disorder, illness,indisposition, infirmity, malady, problem, or sickness. We use the worddisease. Some diseases, such as pregnancy, can actually be joyous newsto the patient.

[0056] Symptom

[0057] As the disease progresses and evolves in the patient, it createsvarious direct and indirect effects that can be noticed by the patientor externally observed or measured. These signs of disease also havevarious names in medicine, such as complaint, effect, indication,manifestation, presentation, problem, sign, or symptom. We use the wordsymptom.

[0058] Doctor

[0059] A person with symptoms, i.e., a patient, typically seeks helpfrom someone trained in medicine, who may be any one of the following:attendant, clinician, dentist, doctor, expert, healthcarer, MD, medic,midwife, nurse, ophthalmologist, optician, paramedic, physician,practitioner, professor, provider, psychiatrist, specialist, surgeon, ortechnician. For our purposes, we use the word doctor in its most generalsense of someone who is trained and experienced in at least some aspectof medicine, as opposed to the general lay public that has no formalmedical training.

[0060] Examination

[0061] In real-world medicine, the doctor usually examines the patientto determine the extent of the symptoms and to make other observationsthat the patient is not trained to notice. In the automated medicalworld of the present diagnostic system, physical examination isnecessarily secondary, since the patient is always remote from the“doctor”. One partial solution is to train the patient (or an associate)to perform self-examination at home, perhaps with the aid of anexamination kit. Another partial solution is to refer the patient to adoctor or to a lab for a specified examination or test. This is oftendone in real-world medicine, when—for example—a doctor refers a patientto a specialist, or sends the patient to a lab for a special test orexamination.

[0062] Laboratory Test

[0063] Some symptoms can only be measured by special chemical,electronic, or mechanical devices which require a specially equippedlaboratory and trained lab technicians. Such labs typically obtain asample of the patient's body fluids or tissue, called a specimen. Theyanalyze the specimen and report the results to the doctor. A key tounderstanding lab tests is that one must ask the lab what test toperform. Contrary to public opinion, a lab does not just test “foreverything.” There are perhaps 1500 different lab tests, and the labwill perform only what is requested. So it is important for the doctorto determine which test to request, and this, in turn, depends on thediagnosis.

[0064] With new monoclonal antibody and polymerase chain reactions (PCR)tests, an ever-increasing number of laboratory tests can be performed bythe patient or an assistant at home. Examples would be urine pregnancytests, a urine dipstick to detect leukocyte esterase, and nitrates todiagnose a urinary tract infection. Diabetics prick their skin to get asmall amount of blood so that a home glucometer can determine theirblood sugar level and, thus, how much insulin to take.

[0065] A home diagnostic kit is available that provides the most currenttechnologies, and may be referenced during consultations with thepatient if the patient has one.

[0066] Imaging Modality

[0067] Some symptoms can be observed by devices that display some partof the body as an image. The best known of these is the x-ray. Othersare ultrasound, CAT scan, MRI scan and PET scan. Imaging modalitiestypically require the presence of the patient to take a “picture” ofsome part or all of the body. The imaging lab takes the picture and mayhave a resident specialist who interprets the image for the doctor. Inany case, the results of the imaging study are forwarded to the doctor.

[0068] Session

[0069] In the automated method utilized by the present diagnosticsystem, the patient contacts the system by phone, the Internet or someother communication mechanism. The patient interfaces with the system,so that the system plays the role of the doctor, and no human doctor isinvolved. One such consultation may be called a session.

[0070] Question

[0071] In the automated approach used by the present diagnostic system,most of the information about a health problem is obtained during asession, by asking the patient (or someone speaking for the patient)questions. Asking a question typically involves a number of elementssuch as introducing a topic, giving background, defining terms,suggesting approximations, asking the actual question, and instructingthe patient how to indicate the response (“press 1 for yes; press 2 forno”). Thus, question generally includes all of these elements. Whenreference is made to the actual question, the term question text may beused.

[0072] Valuation

[0073] In addition to questions, health data can be obtained by variouscomputational algorithms such as sorting and searching, comparing andmatching, mathematical or graphical calculations, logical inferences, orlooking up data in tables and databases. In the automated method, theword valuation may be used for all computations of health data that donot involve the patient. A simple example is to classify the patient'sage into labels used in diagnoses, such as Newborn, Infant, Child,Teenager, Adult, Senior, and so on. Once the system obtains thepatient's age from the patient, it does not need to ask the patientwhether he or she is a teenager; this can be done internally, usingvaluator objects.

[0074] Diagnosis

[0075] In real-world medicine, after the doctor has accumulated thenecessary health data from and about the patient, the doctor makes adiagnosis, which means that the doctor identifies the patient's sicknessfor the purpose of treatment. For every chief complaint, the doctorknows a differential diagnosis, which is a relatively short list ofpossible diagnoses. After the doctor has accumulated the necessaryhealth data from and about the patient, the doctor has at the least, apatient specific differential diagnosis, and hopefully “the” diagnosis.In the automated method of the diagnostic system, the softwareestablishes the (differential) diagnosis by comparing the patient'ssymptoms to its database of diseases and symptoms.

[0076] Treatment

[0077] When the doctor has established the diagnosis, the doctor cantake steps to heal the patient. As always, there are many words for thissuch as advice, counseling, first aid, health care, healing,intervention, medication, nursing, prescription, rehabilitation,surgery, therapy, and treatment. For all of these, this document may usethe word treatment.

[0078] Disease Management

[0079] Some diseases may require continuing treatment and repeatedexaminations for months or years, or even for the patient's lifetime.Such long-term treatment is called disease management.

[0080] Object

[0081] In computer software terms, an object is combination of data andprocesses that manipulate the data. The data are said to be“encapsulated,” meaning that they are hidden, so that a user of theobject only sees processes that can be invoked. Using an object'sprocesses, one can then manipulate the data without having to know theexact location and format of the data. When more than one copy of theobject is required, one can make copies of the data, but use the sameprocess set to manipulate each of the copies as needed. This set ofprocesses can then be thought of as an “engine” that controls orrepresents the objects'behavior, whether there are 10 or 10,000 objectcopies.

II. OBJECT-BASED MEDICAL DIAGNOSIS

[0082] This section describes a new diagnostic paradigm that usessoftware objects to establish a broad, generalized software environmentfor medical diagnosis, which is used to define and develop theprogramming elements of medical diagnosis. The objects are then used toguide and control the diagnostic process, to conduct the patientinterview, to perform related analytical tasks, and to generate thediagnoses. A software object is a fundamental software structure thatcan be used to organize the processes and data of a computer program insuch a way as to make possible very complicated applications. Thisdescription will discuss novel uses of object oriented programming (OOP)in medical diagnosis, such as the use of software objects for thepurpose of fully automated medical diagnosis, the entire/overall methodof dynamically assembling the components of diagnosis in the form ofobjects, and then letting the objects interact to compute a diagnosis.

[0083] Defining and creating software objects is well-known to anyprogrammer trained in object-oriented programming. Using an OOP-capablecompiler, the programmer defines the data that represent the object andthe actions that the object can perform. At run time, the programcreates an object, supplies the data that define the object, and thenmanipulates the object using the object actions. The program can createany number of objects as needed. Each object can be independentlyinitialized, manipulated, and destroyed.

III. THE OBJECT-BASED METHOD

[0084] In the object-based (OB) method discussed here, software objectsare used as active intelligent agents that represent all of the playersand all of the data in suitably organized roles. It is important to notein this metaphor that all of the disease objects, which are“specialists” for a single disease, are allowed to monitor the questionsand answers of other objects.

[0085] One key concept of the OB method is to think of disease andsymptom objects as representing the medical experts inside the computer.If we ask the Appendicitis Disease Object to look at a patient, theobject looks at the patient data, notes that the patient does indeedcomplain of abdominal pain and nausea—but then “notices” theappendectomy scar! Obviously, appendicitis can be ruled out,; butinstead of shrugging its shoulders and giving up, the AppendicitisDisease Object now invokes another disease object that is an expert in,say, Small Bowel Obstruction. That object takes a look, asks somequestions, and passes the patient on to still other disease objects. Ineffect, a huge number of diagnostic experts are gathered at thepatient's bedside, and each object gets a turn at evaluating the patientdata in terms of its own symptom pattern.

[0086] As an actual patient symptom set is built up, disease objectsjudge themselves and judge the likelihood of other diseases. Theemergent effect is a patient interview and a diagnostic evaluationthat—by design—constantly stays focused on the most likely set ofdiseases of the patient. Carefully focused questions are used toeliminate or reduce the likelihood of diseases, to promote others intothe “realm of suspicion,” and to expand the search in a promisingdirection, based on the data being obtained from the patient.

[0087] Object Overview

[0088] A software “object” is basically a data structure plus associatedprocesses that can do things with or for or to the data. An importantproperty of an object is that the object's data can be hidden behind theobject's processes, so that the outside user of the object can only seeand use object processes that can be invoked to access the data. Theobject is said to “hide” data; it provides the powerful ability ofdecoupling the world that uses an object from the object itself.

[0089] Now assume an object is a “smart doctor”. Not one that knowseverything about medicine, but just one that knows about, say, “MalariaVivax in immunocompetant female of child-bearing age that is resident ofUpper Gabon”. This object knows nothing about anything other thaneverything about one tiny portion of one specific disease. Next, thisdisease object is trained to diagnose a patient. It is free to accessthe patient's medical record, to ask the patient questions, to ask forcertain lab tests, and to compare the patient to other patients in thesame household or region (e.g., to detect infection or epidemic). Intrue OOP fashion, the disease object does not actually ask the patient,but it calls a Symptom Object which calls a Question Object, which usesa Node Object, which interfaces with a Patient Object, which interfaceswith the Communication Object which interfaces with the Port Object andso on. One of the objects, somewhere down the hierarchy actuallydisplays a message on a screen, or speaks a question into a telephonemodem, or sends a question on a fax machine.

[0090] Suppose thousands of disease objects have been defined, each witha well-defined, different, specific disease in mind. One disease objectis not necessarily matched with one disease, but divide each diseaseinto its major phases. Define Appendicitis as, say, three diseaseobjects: (1) early, pre-RLQ-Pain Appendicitis, (2) middle Appendicitis,from RLQ pain to Rupture, and (3) late, post-rupture Appendicitis. Letthese three objects interview a patient and compete with each other asto the condition of the patient.

[0091] Now define thousands of symptom objects, each for a differentspecific symptom. Again, divide complex symptoms into less complex ones,so that they build on each other. Define Cough into, say, 12 types thatpatients can identify and doctors can use to diagnose. Define fever intouseful levels. Define pain into PQRST codes.

[0092] Now define a Diagnostic Engine Object, much like the List-Based(LB) Engine described in U.S. Pat. No. 5,935,060, which is smart enoughto pit these objects against each other, in a race to be the first todiagnose itself. The engine is built to be smart enough to switch amongthe disease and symptom objects, so that nobody monopolizes thediagnosis. It is smart enough to know when to stop, to know when it isused for testing and when an emergency patient is online.

[0093] The following objects are described as examples of the kinds ofobjects to be utilized by the system.

[0094] A. Disease Object

[0095] A Disease Object (DO) is a software object that represents anabnormal health state (illness, disease, disorder, cause) which wecollectively call a “disease.” It is used in the method to establish thelikelihood that the specified disease exists in the current patient.

[0096] The object's data are the basic properties of the disease and itsruntime state flags, such as:

[0097] the name of the disease,

[0098] its identification code,

[0099] its prevalence in the patient population,

[0100] a list of the component symptoms,

[0101] a list of symptom values and their diagnostic weights,

[0102] a list of alternative symptom values and their weights,

[0103] a list of synergies and their weights,

[0104] threshold values to be used,

[0105] a list of symptom values established in the patient,

[0106] the diagnostic score for the current patient.

[0107] The object's actions are the numerous functions and proceduresneeded by the system to manipulate a disease, such as processes to:

[0108] pre-test the disease elements,

[0109] print the disease elements for review/editing by the author,

[0110] reset the disease for a new patient,

[0111] load disease data from the patient medical record (PMR),

[0112] diagnose the disease in the current patient,

[0113] report the diagnostic score,

[0114] write disease data to the PMR.

[0115] One of the Disease Object's built-in procedures is called“Diagnose”—a function that diagnoses the current patient for the diseasein question. This function invokes the Valuate function of one or moresymptom objects, which in turn invoke valuator objects to establish thevalue of symptoms by looking in the PMR, by calculating a formula, or byinvoking a question object to ask the patient a question. Perhaps abetter way to present the diagnostic object is as a medically-trainedsoftware robot, an idiot savant that knows everything there is to knowabout only one disease, and knows how to sniff it out in a patient.

[0116] The DO is used to capture all that is known about a given diseasefrom an author and other sources, and to diagnose the disease in apatient at run time. The DO can also be used to represent severalrelated diseases that share common symptoms but diverge at some level ofdetail. For example, Malaria Falciparum, Malaria Ovale, Malaria Vivax,and Malaria Malariae might be combined into a DO Malaria and used toestablish or rule out the basic symptoms of malaria before getting intodetails.

[0117] The DO can be used to subdivide a complicated disease intosmaller diseases. For example, is might be useful to divide Malaria into(1) Malaria in non-immunocompetant patients and (2) Malaria inimmunocompetant patients, to capture the different detailedmanifestations of the disease in these patient types.

[0118] B. Symptom Object

[0119] A Symptom Object (SO) is a software object that represents apatient health item (sign, symptom, complaint, presentation,manifestation, finding, laboratory test result (home or remote),interpretations of an imaging study) which is collectively called a“symptom.” It is used in the system to describe patient health in termsthat the LB system can use for diagnosis.

[0120] The object's data are the basic properties of the symptom and itsruntime state flags, such as:

[0121] the name of the symptom,

[0122] the type of symptom values (numeric, words, graphic),

[0123] the valid symptom values (NONE, LOW, MEDIUM, HIGH),

[0124] the name of the valuator object used to elicit the values,

[0125] the actual (runtime) values, over time, in the current patient.

[0126] The object's actions are the numerous functions and proceduresneeded by the system to manipulate a symptom, such as processes to:

[0127] pre-test the symptom elements,

[0128] print the symptom elements for review/editing by the author,

[0129] reset the symptom for a new patient,

[0130] read/write past symptom data from/to the PMR,

[0131] valuate, i.e., establish the symptom value in the currentpatient,

[0132] report the symptom value,

[0133] report time-based synergy values (onset/offset, slope, trend,curve, area).

[0134] The SO may be thought of as a sort of software robot that knowseverything about one specific symptom, how to establish at a specifiedtime in the patient, and how to report it as specific values.

[0135] One view of the SO is as the fundamental unit of medicaldiagnosis, the quantum that is used to interface theoretical knowledgeabout disease to actual manifestation of disease in the patient.

[0136] Another view is that the SO plays the role of the variable in the“script language” of the method. By weighting them, the authorestablishes values of variables to look for, and by summing the valueweights, the system finds the diseases of interest to the author.

[0137] The basic use of the SO is to encapsulate all that is known abouta given symptom, at any point in the patient's lifetime. Symptom valuesare the units that are weighted in assessing the presence of disease inthe patient. The SO can also be used as a syndrome, to collect severalsymptoms that have medical significance as a group.

[0138] A symptom object describes the data and processing elements ofany data item that can contribute to diagnosing a patient's disease. Inreal-world medical terms, a symptom object is known (depending on itscontext) as a symptom, sign, complaint, observation, test result,manifestation, report, presentation, and so on. In programming terms, asymptom object is a variable that can take on a specified range ofvalues in a patient.

[0139] Having said that, it is very important to understand that aSymptom Object is not limited to the classic signs and symptoms ofmedicine. While classic symptoms (e.g., pain, fever, headache) obviouslyplay a significant role in scripts, the Symptom Object is also used tomanipulate other data that somehow contribute to diagnosis, such as thepatient's habits, culture, environment, and even education. In addition,symptom objects can be used to build artificial internal data structuresas necessary. Thus, a symptom object can be used to define specialgroups of symptoms (syndromes, if you will), or to control the exactsequence in which the system elicits certain symptoms from the patient,or simply as convenient software containers that perform somecomputations or a table lookup to obtain a value. The Symptom Object isthe “work horse” of scripting, and this is reflected by the fact thatmany symptom objects are collected into a central system database thatcan be shared by all script authors, via the Internet.

[0140] A symptom object consists of the software elements needed tocalculate a “value” that is used for diagnosing a given disease. Valuesare typically obtained by asking the patient one or more questions, butthey can be obtained in other ways as well:

[0141] by accessing the patient's medical record,

[0142] by accessing the patient's responses to previous questions inthis session,

[0143] by logical reasoning, using specified implication formulas,

[0144] by mathematical computation, using specified formulas.

[0145] If a symptom value has been established by other means (such asfrom the medical record or by implication), the question will not beasked. For example, once the patient's birth date is known, thepatient's age will not have to be asked again.

[0146] In alphabetical order, exemplary aspects of a Symptom Object areas follows: Answerability probability that the patient knows the symptomClass what kind of symptom this is (history, sign, custom, logic.)Documentation description and development history of the symptom objectICD ICD-9CM code for the symptom Keywords search words to find symptomobject in the index Label name of the symptom object (not the symptom)Location where the symptom can be obtained Name formal medical name ofthe symptom Onset_Offset special onset/offset attributes Persistence howlong a value is good for, once obtained SNOMED classification code forindexing the entire medical vocabulary, including signs, symptoms,diagnoses, and procedures Synonyms alternate names of the symptomTrending special trending information such as the changes in severity ofa symptom with time or the evolution of symptoms in a disease processValuator label of the object that actually obtains the symptom valuesValue current value of symptom Value_Date date of last Value Value_Timetime of last Value Value_Type operational type of the value (integer,real, text, discrete)

C. Valuator Object

[0147] A Valuator Object (VO) is a software object that represents theactions required to establish the value of a symptom in a patient at aspecified time.

[0148] The VO data are the basic properties of the symptom and itsruntime state flags, such as:

[0149] the type of valuation used (question, formula, graph, table),

[0150] the type of value reported (numeric, words, graphic),

[0151] the valid symptom values (NONE, LOW, MEDIUM, HIGH),

[0152] if applicable, the question object to be used,

[0153] if applicable, the mathematical or logical formula used,

[0154] if applicable, the graph, or table, or database to be used.

[0155] The VO actions are the functions and procedures needed by thesystem to manipulate a value, such as processes to:

[0156] pre-test the valuator,

[0157] print the valuator formulas for review/editing by the author,

[0158] establish the value in the current patient,

[0159] report the value.

[0160] The basic use of the VO is as an interface between the symptomand the patient level of abstraction. The VO can be used to presentdummy patients to the LB system for testing. The VO can be used toswitch among lookup tables, based on global system control setting. TheVO makes an object out of an action, a common use of objects, so that wecan globally describe and control the actions that take place at somelower level.

[0161] D. Question Object

[0162] A Question Object (QO) is a software object that describes thesoftware elements required to establish a mini-dialog of questions andresponses with the patient, in order to obtain a symptom value. It isthe task of the QO to select the appropriate question set, to invoke theappropriate node objects that actually question the patient, and toreport back the patient's response. A QO is a type of valuator objectthat specializes in interaction with a patient.

[0163] The Question Object is the point in defining a script where theauthor actually writes a script, albeit typically a very short one, thatis focused on asking about one specific symptom. This mini-script isbroken down into separate node objects, each of which presents aPreamble, a Question, and a set of labeled Buttons to the patient, andobtains a response from the patient. The QO data are those elementsrequired to ask a question and obtain a response from a patient, such asthe list of node objects to be used.

[0164] The object's actions are the functions and procedures needed bythe system to manipulate a question, such as processes to:

[0165] pre-test the question and node elements,

[0166] print the question elements for review/editing by the author,

[0167] ask the question and report the response,

[0168] specify the actual natural language text to be used,

[0169] establish the user interface required for the current platform,

[0170] invoke a node object to actually ask the question and report theresponse.

[0171] The QO is another interface object, used to separate thequestioner from the language used to question the patient. The basic useof the QO is to handle the details required to present a (possiblycomplex) question to an online patient. The QO can be used to change theeducational level of the question text (Question Roller). The QO can beused to change natural language used to speak to the patient.

[0172] E. Node Object

[0173] A Node Object (NO) is a software object that describes thesoftware elements required to ask a single, well-defined question of thepatient and to return the response selected by the patient. It is thetask of the NO to present the required data to the GUI in a form thatwill appear user-friendly manner on the user display, to wait anappropriate amount of time for a user response, to possibly re-promptthe user, and to ultimately return the user's response.

[0174] Node objects operate at the lowest level of the script hierarchy;they interface to the operating system's user interface. Computationdepends on the platform used. For a Windows operating environment, thenode would display an appropriate window containing sub-windows for thePreamble and Question Text. Next it would display the requisite numberof buttons and display the form to the user. When a button is pressed bythe user, the node object returns the index number of the response.

[0175] The NO data are those elements required to ask one detailedquestion, obtain a response, and return the index number of theresponse. The NO's actions are the functions and procedures needed bythe system to display a question to the user, such as processes to:

[0176] pre-test the node elements,

[0177] print the node elements for review/editing by the author,

[0178] display the question and report the response.

[0179] The NO is another interface, between the script objects and thepatient. The basic use of the NO is to handle the low-level detailsrequired to “talk” to a patient. The NO can be used to port anapplication to another hardware platform or operating system. The NO canbe used to “fake” a patient by taking inputs from a test file andwriting outputs to a test result file. The NO can be used to log allquestions to, and responses from, the patient, time-stamped to thenearest hundredths of a second if necessary. One of the reasons fordefining Node Objects as well as Questions Objects is that the entiresystem can be translated into other languages by translating all of theNode Objects.

IV. LIST-BASED ENGINE CONCEPTS

[0180] In one embodiment of the invention, the List-Based Engine (LBE)is one embodiment of the diagnostic processing method. It is a programthat, essentially, takes a set of diseases (more precisely a collectionof disease descriptions, symptom definitions, and questionspecifications) and processes them against one specific patient.

[0181] The patient is typically a human that can conduct an interactivedialog with the system and can respond to questions posed by the system.Alternatively, the patient may be represented by a medical record inwhich some or all symptoms already have values, so that the systemsimply sifts the values and scores the diseases accordingly. For testpurposes, the patient may even be represented by a computer program thatis “playing patient” in order to test the system's ability to respond toabnormal situations such as unexpected key presses, extensive responsedelays, contradictory answers, requests for repeating a question, andabnormal termination of a session.

[0182] For a specific run or session, the system begins its work bygathering a set of candidate diseases that it is supposed to diagnose.This initial candidate list is most likely assembled by a module thathas analyzed the patient's Chief Complaint and selected appropriatediseases from a database that is indexed by chief complaint. In theabsence of a chief complaint, the system can just start with all thediseases it finds in a given project file, where an author wants to testa newly created or edited script.

[0183] Once it has a list of candidate diseases, the system's job is toprocess these diseases, typically by asking questions and accumulatingdiagnostic scores for each disease until some specified system goal isreached. This system goal is expressed by the system “Mission” setting,which can specify various goals such as “run all diseases” or “run untilthe first disease is ruled in” or “run until 10 minutes have passed”,and so on. The default system mission is to “run all diseases until allsymptoms have been evaluated”.

[0184] Diagnostic Loop

[0185] In one embodiment, the system uses a “diagnostic loop” to processthe current disease list. Portions of the diagnostic loop have beendescribed in Applicant's U.S. Pat. No. 5,935,060, which is herebyincorporated by reference. The diagnostic loop consists of a series ofiterations in which the system considers its mission in the light of thelatest status of all candidate diseases. Depending on the mission, thesystem can perform all kinds of special calculations and evaluationsduring this loop. The loop actually consists of several nested loopsthat may involve recursion to evaluate subordinate symptoms.

[0186] Current Disease

[0187] In one embodiment, during the diagnostic loop, the first aim ofthe system is to determine which disease it should evaluate next, basedon its mission. The mission might be to “evaluate the disease with thehighest score”, or “evaluate the disease with the highest diagnosticmomentum”, or “evaluate any random disease”. The default mission is toevaluate the next disease as originally given in the candidate list.

[0188] Current Symptom

[0189] In one embodiment, once it has a “current disease”, the nextsystem aim is to determine which symptom of the current disease itshould evaluate next. The mission might be to “evaluate the symptom thatcan add the highest weight to the score of the current disease”. A morecomplex mission might be to “evaluate the symptom that will advance thescore of the most diseases”. The default mission is to evaluate the nextsymptom in the symptom list of the current disease.

[0190] Current Evaluation

[0191] In one embodiment, evaluating a symptom consists of establishingthe value of the symptom for a specified date and time in the patient'slife. How this is done depends on the type of symptom and on the type ofthe valuator object defined for the symptom. A symptom may already havea valid current value in the patient's medical record. For example, thepatient's gender may already be in the medical record, in which case thesystem obtains it and continues. The patient may already have suppliedthe symptom value during the current session in the context of aquestion for some other disease. Again, the system obtains the symptomvalue from the current session record. (This feature avoids asking thepatient the same question in the process of evaluating differentdiseases.) Many symptoms are evaluated by running a Question object,i.e., asking the patient one or more questions. Symptoms may use a Logicobject to evaluate a value; this means that the system parses and runs alogic formula, such as “if patient has symptom value A and has symptomvalue B, then the value of this symptom is C”. To evaluate this symptom,the system would (recursively) evaluate symptoms A and B and thenestablish C, if appropriate.

[0192] Scoring

[0193] In one embodiment, after the system establishes a new symptomvalue, it updates the scores of all candidate diseases. Depending on thedescription of each disease, scoring can consist of simply adding theweight corresponding to the new current symptom value, or it can involveadding special synergy weights based on the values of other symptoms, oron the timing of symptoms. Scoring can also include establishingprobabilities of diagnosis, which typically depend on the existence ofseveral symptom values, sometimes in a defined time order. Finally,scoring includes evaluating the scores of diseases against variousthresholds. Depending on the system goals, a disease may be placed intoa special category based on its score. For example, a disease may bedeemed “ruled in” when its score reaches or exceeds a specifiedthreshold, or it may be placed on a special diagnostic momentum track ifits score is increasing more rapidly than other disease scores. Thedefault system goal is to add the symptom weights to all applicabledisease scores.

[0194] Continuation

[0195] In one embodiment, after the system has updated the scores of alldiseases, it determines how to continue by considering the set of newscores. Again, the system's goals can specify different actions for thesystem, such as “stop when any score exceeds 1000” or “stop when thediagnosis has been ruled in” or “stop when the system has the five mostlikely diagnoses” or “stop when ten minutes have elapsed”. The defaultgoal is to run until all symptoms of all diseases have been evaluated.

[0196] A. Dynamic Rules and Goals

[0197] In one embodiment, the system is designed so that the rules,limits, and goals that govern the diagnosis can be changed at run time.The system may use tables of rules and goals and limits, of which theapplicable set is selected as needed.

[0198] For example, at the top of the diagnostic loop when the systemselects the next disease to be considered, it can use any one of anumber of rules such as “Select the disease that:

[0199] is the most life-threatening disease remaining to be diagnosed,

[0200] shares the most symptoms with other diseases,

[0201] has the highest current diagnostic score,

[0202] has the highest current change in diagnostic score,

[0203] has the fewest unresolved symptoms,

[0204] is next in some order specified by the author.”

[0205] Similarly, when the system selects the next symptom with adisease, it can choose it based on various dynamic modes or controlvariables.

[0206] The patient him/herself can set certain boundary conditions onthe consultation. Several examples include:

[0207] a patient who has only 20 minutes to talk

[0208] a patient who wants only to exclude a certain disease (“e.g., myfriend had a headache like mine, and he was diagnosed with brain tumor”)

[0209] B. Diagnostic Momentum

[0210] In one embodiment, the “diagnostic momentum” is the rate ofchange of the diagnostic score of a candidate disease. It provides ameasure of how fast a given disease is accumulating diagnostic weights,compared to other competing candidate diseases. The system tracks thescore and the momentum for all candidate diseases and can use thisinformation to change the diagnostic mode. Note that the use of varioussynergy weights will add extra weight to diseases with many matchingsymptoms, so that positive feedback is established that tends to favordiseases with many matching symptoms and thus to converge rapidly on onedisease (see e.g., Sequencing Synergy and Summation Synergy).

[0211] As the LB method diagnoses, it tracks for each disease the latestdiagnostic score, the last change in the score, and the name of thedisease with the largest momentum during the current iteration of thediagnostic loop.

[0212] Since the name of the disease with the highest momentum isavailable at all times to the LB engine, it can be used to guide thediagnostic process itself and to check whether any goals or limits ordecision points have been reached. It provides the LB method withfeedback that lets it feel its way along a diagnostic path in a mannerthat is strongly driven by the patient's responses. For example, thefaster a disease is approaching the diagnostic threshold, the moreintensely the LB method can focus on disease.

[0213] This feature simulates the manner in which a human doctor siftshis knowledge of disease based on what s/he is learning about thepatient's condition. As the symptom pattern begins to match the patternof a specific disease, the doctor will ask questions designed to confirm(or reject) this disease.

[0214] The advantage of the momentum feature is that it (1) quicklyde-emphasizes many less relevant diseases, (2) minimizes questions askedof the patient, and (3) cannot be done as rapidly and as precisely bythe human doctor as by the computer.

[0215] C. Horizontal Axis of Inquiry (HAI)

[0216] In one embodiment, the system conducts its diagnostic inquiriesalong various “axis”, i.e., lines of investigation or focus directions.We call two of these strategies the Horizontal Axis of Inquiry (HAI) andthe Vertical Axis of Inquiry (VAI). This section focuses on the HAI.Note: The “inquiry axis” terminology relates to the manner in which thesystem selects the next focus symptom. The terminology derives from theDisease/Symptom Matrix (DSM) metaphor, in which a table is formed byarranging the candidate diseases as side-by-side columns (hence“vertical”) and the component symptoms as rows (hence “horizontal”). Seethe DSM figure. In database terminology, fields are arranged along thevertical, and records arranged along the horizontal. The Horizontal Axisof Inquiry (HAI) strategy is a diagnostic mode that focuses on quicklyeliminating inapplicable diseases from a large list of candidates. HAIis typically used early in a diagnostic session, when the system hasnumerous candidate diseases and selects focus symptoms based more on howmany diseases contain the symptom than on how effective the symptom willbe in identifying one disease.

[0217] Other diagnostic methods have one and only one method. Thepresent invention, by contrast, allow many different modes of inquiry,which are themselves dependent on the progress of the diagnosis. In boththe HAI and VAI strategies, the LB engine updates the scores of allcandidate disease scores with the responses obtained from the patient.Thus, the differences in these strategies relate primarily to how thesystem selects the next focus symptom, not how the candidate diseasescores are updated.

[0218] In the HAI mode, the Alternative Symptom (AS) feature willtypically be activated, so that fewer and more general questions tend tobe asked. In the VAI mode, the AS feature may or may not be activated,depending on the need for more detailed responses from the patient.

[0219] The choice between HAI and VAI strategies is very importantbecause it permits general “sifting” of many candidate diseases as wellas focusing on diagnosis of one specific disease, at a detail levelwhere the patient can—indirectly—interact with the script author, aworld specialist on that disease. Other medical diagnostic systemstypically interact at one and only one level with the patient.

[0220] The decision which one of these (or some other) strategies ormodes to select can be programmed to depend on any number of variables.For example:

[0221] it may be specified by the process that calls the system;

[0222] it may be modified based on the goal selection routine that runsearly in the consultation;

[0223] it may be switched based on the Diagnostic Score or Momentumreached by one or more diseases;

[0224] it may be switched by various computations related to the ChiefComplaint or the First Significant Symptom;

[0225] it may be switched based on a new response by the patient, whichnegates or significantly modifies a previous response.

[0226] In the HAI strategy, the system searches the list of candidatediseases and their symptom lists to find symptoms shared by manydiseases. It selects such a shared symptom and evaluates it, typicallyby asking a question, or by evaluating a formula or a logic structure.Then it updates every disease with the new value of the symptom, andadds the appropriate weights to each disease score.

[0227] In the HAI strategy, the system can sort the candidate diseasesby the number of shared symptoms to prepare for an efficient subsequentelimination process. For example, by establishing the gender of thepatient, the system can eliminate all gender-specific disease. The HAIstrategy permits the system to partition the candidate diseases intouseful classes so that it can focus on promising classes first. Forexample, it might partition diseases into the following categories:urgent, serious, common, or it might partition diseases into promising(high probability that the diagnosis is among them), intermediate andlow probability.

[0228] D. Vertical Axis oF Inquiry (VAI)

[0229] The Vertical Axis of Inquiry (VAI) strategy is used to examineone candidate disease in detail, so that the system selects the nextfocus symptom repeatedly from the same disease. This strategy isintended to give one specific disease that has scored significantly thechance of establishing itself as a diagnosis. The VAI strategy isequivalent to letting the script author (1) ask several successivequestions about this disease and (2) ask his or her preferred questions,in cases where the patient has previously answered Alternative Symptoms.

[0230] In the VAI strategy, the LB engine evaluates the various symptomsof one disease. Symptoms can be selected in various orders, depending onthe engine mode. In one embodiment, the script author may prescribe asequence in which symptoms are evaluated, but this can be overridden byfirst asking for symptoms that carry the most weight, or for symptomsthat are the easiest or fastest for the patient to answer. The systemselects such a shared symptom and evaluates it, typically by asking aquestion, or by evaluating a formula or a logic structure. Then itupdates every disease with the new value of the symptom, and adds theappropriate weights to each disease score.

[0231] In the VAI strategy, a patient who has earlier answered questionsusing Alternative Symptoms (see there) can now have the opportunity tobe asked the symptoms which the author defined. This has the effect of“fine-tuning” the answers to the specific disease at the point when thedisease is becoming a contender. In this way, a patient can be promisedthat no matter what disease they have (if the system covers thatdisease) they can be guaranteed to interact with a dialogue created by aworld-class specialist in that disease.

[0232] The VAI strategy can be set to use only the author's ownsymptoms, instead of accepting the (normally alternative) symptoms ofother authors. This means that the system can (perhaps at the patient'srequest) re-ask all symptoms using only the authors' own questions.This, in turn, means that the patient's entire consultation on a givendisease can ultimately be conducted using the world expert on thedisease. This gives the LB method the ability to shift from the broad,generalizing viewpoint (where it accepts all alternative symptoms) tothe narrow, specific viewpoint, where the world expert's questionsphrasing may help to distinguish among close diseases.

[0233] The HAI and VAI strategies are part of the central processes forsymptom selection of the system, specifically the LB Diagnostic Loop.The decision which one of these (or some other) strategies or modes toselect can be programmed to depend on any number of variables. Forexample, it may be specified by the process that calls the LB engine; itmay be switched based on the Diagnostic Score or Momentum reached by oneor more diseases; it may be switched by various computations related tothe Chief Complaint or the First Significant Symptom; it may be switchedbased on a new response by the patient, which negates or significantlymodifies a previous response.

[0234] The VAI and HAI strategies permit the system to vary itsdiagnostic focus from the general to the specific. In the early stages,the engine knows little about the patient and must ask the best generalquestions that quickly eliminate large numbers of candidate diseases.But after applying the HAI strategy for a while, if the diagnosticmomentum of some disease D reaches a specified level, the engine canthen switch to the VAI strategy to focus diagnosis on disease D, to themomentary exclusion of all other diseases. It is important to note thatall of the disease object (experts) “monitor” all of the questions andanswers generated by other disease objects. After applying VAI for awhile, disease D may emerge as the “front runner”, or it may fade, beingoutstripped by one or more other disease scores. One of these may thenbecome the driver of another VAI round, or the diagnostic strategy mayrevert to HAI if no disease has a clear lead.

[0235] There is a powerful holistic effect when various LB features suchas Disease Momentum, Dynamic Goals, HAI, VAI, Alternative Symptoms, andSynergy Weighting are combined. Consider how the LB engine sifts thecandidate diseases and converges on the appropriate ones: As one diseasegains score and momentum in the HAI strategy, this triggers a shift toVAI strategy. If the system is “on the right track”, the VAI strategywill rapidly confirm that several key symptoms of that disease arepresent in the patient. Through the various Synergy weights, thisconfirmation will increase the score and the momentum, and reinforce thecycle to converge on the focus disease as a diagnosis. In oneembodiment, the symptom weights may be increased when the system isoperating using the VAI strategy. This feature allows the system toaccommodate Bayesian probabilities in the evaluation process. On theother hand, if the system is “on a cold trail”, the VAI strategy willfail to confirm additional symptoms, the disease score will lag behindthose of other diseases (which are being updated in parallel) and thesystem will soon abandon this fruitless pursuit and either return to theHAI strategy, or select another disease for a VAI inquiry.

[0236] E. Alternative Symptoms

[0237] In one embodiment, the Alternative Symptom feature of the LBmethod lets a disease author specify a set of symptoms that arealternative to a specified symptom for the purpose of diagnosis. Theinvention lets an author specify alternative symptoms that can take theplace of the author's preferred or specified symptom, perhaps with adifferent weight. The feature is designed to solve the problem thatdifferent authors may prefer different ways of asking a patient aboutthe same symptom, yet we do not want the patient to have to answerquestions about the same symptom over and over.

[0238] The LB engine is programmed with alternate modes that either door do not permit symptom alternatives. When permitted, the systemaccepts the value of any alternative symptom as the value of a symptom;if not permitted, the system requires the asking of the author'sspecified symptom, even if this requires asking the patient certainquestions a second time. One goal of the LB diagnostic method is that,no matter which disease the patient has, he or she will be askedquestions by a world-class specialist in that disease. This featuremimics how human doctors interview a patient: In the early part, thedoctor asks broad questions that determine the general overall nature ofthe patient's disorder. Once one disease emerges as a likely diagnosis,the doctor asks more specific questions that confirm or reject thehypothesis to some degree. Finally, when the most likely diagnosis seemsalmost obvious, the doctor asks even more detailed questions in order torepeat, emphasize, seek more details, add confirming symptoms, and soon. These final question may well repeat questions asked earlier,perhaps to give the patient a final chance to confirm earlier responses.

[0239] The Alternative Symptoms feature gives the patient the option togo back and answer questions exactly as worded by the original diseasescript author, or to simply accept the questioning by the alternativesymptoms. This is analogous to a computer user who installs anapplication who can either insist on a “custom installation” or acceptthe “typical installation”.

[0240] At scripting time, when the author of a disease script firstlists the component symptoms of the disease, the author can eitherspecify brand new symptoms, which the author writes from scratch, orspecify existing symptoms, which the author retrieves from a database ofstored or archived symptoms that is shared by all authors. This initialsymptom set becomes the author's preferred or specified symptoms, whichthe author prefers to be asked of the patient. Next, the author reviewsthe symptom database to see which symptoms are so “close” to his/herspecified symptoms that they can serve as alternatives. The author liststhese alternative symptoms and assigns some diagnostic weight to them.One author's specified symptom is another author's alternative symptom.Thus all symptoms are specified symptoms to some author, who isresponsible for maintaining their currency.

[0241] Each of the authors may be linked by a data communicationsnetwork such as the Internet. When a new symptom object is created byAuthor A, a copy of the new symptom object is instantly “sent” to theauthors of the diseases in which his symptom is also used, e.g., AuthorB. This then would be an alternative symptom for Author B. Author B thenassigns a weight for the disease he is authoring when this newalternative symptom is used in a question.

[0242] At run time, the system can either allow or disallow the use ofalternative symptoms. If the system is in the alternative symptom mode,and the system is seeking the value of specified symptom S1, it mayaccept the value of any alternative symptom in its place. The effect isthat, if the patient has already been asked about any alternativesymptom S2, S3, or S4, the system will not ask the patient again, butwill accept the alternative symptom and its weight. If the system is notin the alternative symptom mode, when the system seeks the value ofspecified symptom S1, it will proceed to ask the questions associatedwith symptom S1.

[0243] The Alternative Symptom feature eliminates redundant questioningof the patient and permits the author to group symptoms together thathave the same impact on his disease. The Alternative Symptom featurelets the author control how she or he wants to focus on symptom details,i.e., on the quantization of symptoms. For high-level diagnosis, a highlevel of quantization may be sufficient; at a later time, the author mayneed more precise details, such as to distinguish between close variantsof a disease.

[0244] In one embodiment, the system symptom database may containseveral thousand symptom script elements, written independently byseveral hundreds of authors. Many of these symptoms may be the same, orbe acceptably similar variations of each other. Without AlternativeSymptoms, the system would load all candidate diseases. In the course ofrunning them, the engine might encounter some of these similar symptomsseveral times. The effect would be to ask the patient the same questionin many different ways, which would be inefficient and would notengender confidence. But with the Alternative Symptom feature, after thesystem evaluates any one of the alternative symptoms, the other symptomsin the set will not be asked.

[0245] A benefit of the object-based system having Symptom Objects andusing the Alternative Symptom feature is that Symptom Objects and theirunderlying objects, e.g., Valuator Objects, Question Objects and NodeObjects, can be “reused”. In one embodiment, the author of a new diseasescript can reuse previously written and debugged objects by a few steps,which may include, for example, renaming one or more of the objects andassigning alternative weights. This object reuse capability permitsfaster coding, testing and release of new disease scripts.

[0246] F. Disease Timeline

[0247] In one embodiment of the invention, the Disease Timeline may be achart or graph that describes how each symptom of the disease manifestsitself over time in a typical patient. The timeline is a characteristic“pattern” of the disease that can be used as a reference for comparisonsof the patient's actual symptom time chart.

[0248] This aspect of the invention relates to pure medical knowledgeabout a disease; it is independent of any one patient. This aspect is“theoretical”, in contrast to a Symptom Time Chart, which relates to the“actual” symptom values as experienced by a patient over time.

[0249] The timeline is for a generic disease, to serve as a basereference. It can be scaled to fit a given patient.

[0250] At design time, the author of a disease object describes thetypical course of the disease in terms of how and when its symptomstypically arise (onset), vary, and subside (offset) over time. Thistimeline starts with the First Significant Symptom (FSS) of the disease,and all timings are based on the start of the FSS. Note that the FSS maybe different than the patient's chief complaint.

[0251] One embodiment utilizes a Gantt chart that records the times ofthe appearance, disappearance, overlap, and other aspects of thecomponent symptoms. Initially, the author might only choose three timepoints for each symptom; later, more and more points can be added. Atypical goal is an hour-by-hour description of the disease.

[0252] At run time, the system matches the patient to the script.Appendicitis may be used as an example disease to walk through a simplediagnosis. Assume that the author has chosen to describe the disease asfollows: The first symptom is often (though not always) anorexia, sothis symptom is the origin for the timeline. Anorexia, then, occurs at 0hour. At hour 1, one typically expects nausea. At hour 3, one expectsepigastric pain to become noticeable to the patient. By hour 8, one canexpect the pain to be migrating to the right lower quadrant of theabdomen, and so on.

[0253] At run time, when a patient enters the system, the systempreferably asks when the chief complaint started. In one embodiment, thesystem then selects the script that is nearest in time. So, here is apatient with appendicitis calling the diagnostic system; she or he may,of course, be at any stage along the disease timeline. Usually anappendicitis patient waits until she or he has abdominal pain beforeseeing a doctor. So, let's say our patient presents abdominal pain of agiven severity as the chief complaint.

[0254] The system (in HAI mode) then searches all candidate scripts forabdominal pain of our patient's severity. It finds the appendicitisscript, which indicates where a patient with that severity should beplaced along the time line. The disease object can now compute the timeoffset required to match the patient, and can “place” or “match” thepatient to that point in time in the appendicitis script.

[0255] Sooner or later, the LB system will let the appendicitis scriptask another symptom. The script will ask the patient about earliernausea or anorexia, and—if the patient confirms—will add weight to thescore of appendicitis. At some point, the rising score will trigger thesystem to switch to VAI mode, and to ask about several more symptomsfrom the appendicitis script. This may rapidly pile on more weight, andthe appendicitis diagnosis would then exceed threshold and would beruled in. If not, the system will know what symptoms should appear next,and let the patient know.

[0256] The chart, graph or timeline described above may also be referredto as a predetermined template of symptom characteristics. One or moreof the established symptoms may have symptom characteristics that arise(onset) or subside (offset) over time so as to match the predeterminedtemplate. If so, additional weight is added to the score for theparticular disease. Furthermore, if the onset or offset characteristicsmatch the predetermined template and a set of the established symptomsoccur in a specified sequence over time, still more additional weight isadded to the score for the particular disease. Thus, it can be seen thatwhen certain symptom conditions are met, the score of a particulardisease may rapidly reach the disease threshold and be ruled-in ordiagnosed.

[0257] A disease needs time to “declare itself.” On one hand, the longerone waits in a disease process, the more certain they can be of thediagnosis; on the other hand, one wants to make the diagnosis as soon aspossible to begin appropriate treatment.

[0258] The author actually has two “clocks”. One clock is related to theappearance of the Chief Complaint, the other clock is related to theappearance of the First Significant Symptom. The HAI mode uses the CCclock, while the VAI mode uses the FSS clock, which is more accurate,but cannot be used until one has a tentative diagnosis.

[0259] See FIG. 31 for an exemplary screen shot of a user interface forspecifying the order of a particular set of symptoms so as to establishthe First Significant Symptom. The user may for example, slide symptombars along the time axis to indicate their particular symptom history.The user would then click on the “submit” button which causes the newsymptom occurrence times to be captured and then evaluated by thesystem.

[0260] The author can also use the symptom timeline as a characteristicpattern of symptom magnitudes. This is useful in describing anddifferentiating diseases based on their symptom patterns.

[0261] G. Spectrum of Terms/PQRST Code

[0262] In one embodiment of the invention, the PQRST Code is acomprehensive method for capturing and encoding a patient's verbaldescription of a symptom. It is particularly suitable for highlysubjective symptoms that are hard to quantify, such as the patient'soverall health, the characterization of a particular pain, or theexpression of a mental state or emotion. The key invention here relatesto the “Vocabulary of Diagnosis.” This refers to the ability of the LBmethod to let an expert author use the exact vocabulary she or he hasdeveloped over years of experience in questioning the patient. In thereal world, certain words used by patients to describe pain are classicindicators of specific disease. In the LB world, this is implemented byletting the patient select from a pick list of words that are thenassociated with a predetermined diagnostic weight. The PQRST Code may beused to track changes in other health data such as lesions, masses,discharges, body functions, mental states, emotions, habits, addictions,and so on.

[0263] Pain is a subjective experience of the patient. It isdiagnostically highly useful, yet practically very hard to describe insufficiently useful detail. The PQRST Code is a comprehensive method forencoding a patient's description of pain, and for using the pain codefor diagnosis in the LB method, and for other purposes such as Advice,Prescription, Treatment, Pain Management, and Disease Management. In theLB diagnostic method, the PQRST Code may be used to encode subjectivesymptom descriptions, to capture changes in symptom descriptions, and toanalyze changes over time. Not only may the PQRST Code itself becomposed of hundreds of elements, but the possible uses of the code inmedical automation are manifold. The PQRST Code is directed tomanipulating medical knowledge in an automated way. The basic idea isusing word spectra and pick lists to capture a patient's subjectivedescription of some health experience. The PQRST Code may then be usedto detect symptom changes, slopes, trends, areas, and so on, wherechange is the key. The PQRST Code feature includes picking words from aword spectrum at two points in time, and then analyzing the significanceof the change, and using this to give extra weight to one diagnosis.This feature places words in the spectra that do show how a particularaspect of pain is likely to change over time, and then does the secondevaluation and gives extra weight to one diagnosis because it manifestedthe expected change.

[0264] The PQRST Code feature includes methods for:

[0265] describing some 20 aspects of pain,

[0266] obtaining these aspects from the patient,

[0267] encoding and decoding these aspects as a single PQRST code,

[0268] using the PQRST code in diagnostic and other contexts.

[0269] At the global level, for all authors and all script, we definesome 20 aspects of pain, such as Quality, Severity, Location, Size,Symmetry, Timing, Localizability, and Migration aspects. For eachaspect, we further define a word spectrum that consists of a set ofwords commonly used by patients to describe that aspect of pain. Forexample, the Quality of pain might be described in terms of “pinprick,knifing, tearing, fullness, tightness, pressure.” The Severity of painwould be rated by the patient on a scale of 0 to 10. Word spectra are,of course, different for different aspects of a symptom. Non-painsymptoms might rank some aspect like “Age” along a numeric scale such as0-7, 8-22, 23-65, and 66 and over. Another spectrum might use words suchas NONE, LOW, MEDIUM, HIGH to characterize an aspect. Or, a wordspectrum might consist of a vocabulary of descriptor words such asPULSING, POUNDING, HAMMERING, TAPPING. The script author definesdiagnostic weights for each word of a spectrum. At run time, a givenspectrum is presented as a pick list from which the patient can choose.The patient picks one word from the list, and the system adds theassociated diagnostic weight to the score.

[0270] The PQRST Code feature permits authors to apply the vocabulary ofdiagnosis that they have developed over years of experience. A scriptcan use several word spectrum symptoms to build up a PQRST Code thatsummarizes the state of health of a patient at some time “t”. This codecan be stored in the patient medical record (PMR) for later use. This isanother example where a symptom object specialized for word spectra maybe defined. The script can collect a PQRST Code for different times T1,T2, T3. The script can then analyze the changes in code over time, andassign weights for significant symptom changes over time. The script canuse the PQRST code to compute synergies based on slope, trend, area,volume, and other properties.

[0271] Frequently during the same consultation, the severity of thepatient's symptoms is trended. In addition, many PQRST array spectra canbe asked at the beginning and at the end of the same consultation. Thereenter function (second consultation for the same disease process) andthe re-reenter function (third consultation for the same problem) areused in concert with the PQRST array to evaluate the evolution of thedisease process to make the diagnosis.

[0272] Each author is able to use or re-use the word spectra that arealready created. Each spectrum is typically 7 to 11 adjectives that arecarefully selected. For example, if a patient's epigastric pain(location) which cannot be localized (localizability) moves (migration)to the right lower quadrant (location) and now is easy to localize(localizability), the patient has appendicitis.

[0273] The diagnostic system can collect and publish medical statisticson the “vocabulary” that is used in diagnosis. The diagnostic system canuse the vocabulary as a “digitized medicine” to fine-tune scripts andtheir actions.

[0274] The following is an example of PQRST Code that tracks the natureof a discharge, instead of a pain. The Mallory-Weiss syndrome consistsof a partial thickness tear in the very inferior aspect of theesophagus. It is caused by severe vomiting. Hence a patient who isvomiting food at time “t” and vomiting food with blood in it one hourlater has Mallory-Weiss syndrome, compared with a patient with a gastriculcer, who would have blood in the vomit from the beginning. Therefore,a symptom built around PQRST encoding of vomit contents will detect theaddition of blood and add the appropriate synergy weight to theMallory-Weiss disorder.

[0275] H. Synergy

[0276] In one embodiment of the invention, and in the context ofautomated medical diagnosis, “synergy” means adding extra diagnosticweight to a disease if a symptom occurs in the patient in a specifiedmanner, intensity, anatomic location, frequency, sequence, combinationwith other symptoms, or similar pattern. The synergy concept provides away for an automated diagnostic system to take into account the symptomsof a patient viewed as a holistic pattern that can be used toincrementally refine the ranking of a disease for the purpose ofdiagnosis.

[0277] The word “synergy” may have the meaning of “combined effect.” Itrefers to accounting for the special additional impact on diagnosis ofthe fact that a symptom is occurring, changing, or interacting withother symptoms in the patient in some well-defined manner with respectto time, anatomic space, quality, sequence, frequency, combination,mutual causation, and so on. In short, the synergy concept implements insoftware the medical fact that the diagnostic significance of acombination of symptoms is much greater than the significance of each ofthe symptoms in isolation.

[0278] For example, applied to the LB diagnostic method, the synergyconcept significantly enhances the capabilities of the method, becausethe weighting mechanism of the LB method can be used to detect andaccount for the presence of synergy in the patient's reported symptoms.In fact, synergy allows the LB engine to dynamically adjust the verydiagnostic process itself after every response from the patient.

[0279] The synergy invention approximates the cognitive process of ahuman medical expert by providing for non-linear weighting of symptoms,by incrementally adding small weights to account for fine differences inpatient health states, by fine-tuning the diagnosis, and by dynamicallyguiding the diagnostic process itself into productive channels.

[0280] Using synergy, the symptom object of the LB method becomes asmart process that does not just store symptom values, but that canperform dynamic intelligent internal analysis of how the symptom behavedover time in the patient, which generates useful diagnostic informationin its own right.

[0281] In the context of certain embodiment of the LB diagnostic method,the word “synergy” has the normal dictionary meaning of “combinedoperation or action”. Again, it refers to measuring the special,additional impact of several symptoms or symptom changes being presentat the same time or in some prescribed sequence. The synergy conceptimplements in software the real-world medical fact that the diagnosticsignificance of a syndrome is much greater than the significance of itscomponent symptoms in isolation.

[0282] As detailed later for every individual synergy type, the synergyconcept significantly enhances the LB method, so that special healthconditions and their changes in a patient can be:

[0283] detected by suitable questioning or computation, and

[0284] assigned diagnostic weights in advance, then

[0285] combined logically and mathematically, and

[0286] used to score candidate diseases, which are

[0287] used to rank candidate diseases, which are finally

[0288] used to select those diseases that the patient most likely has.

[0289] The system diagnostic methods include a novel and non-obvious wayto compute a medical diagnosis. By this method, an medical script authorcan describe in advance certain specific health conditions and effectsin a patient which tend to be less obvious and more difficult to detectby other methods. In certain embodiments of the present automatedmedical diagnosis system, synergy means special manifestations in theanatomic systems, over time, of patient-specific symptoms andpatient-driven verbal descriptions of symptoms. The synergy inventionmimics human cognition by providing for non-linear weighting ofdiagnoses by incrementally adding fine differences in health states, byfine-tuning the initial diagnoses, i.e., by allowing the medicaljudgments of the script authors to be implemented in an automatedmanner. Synergy allows the LB engine to watch and to dynamically adjustthe very diagnostic process itself after every response from thepatient.

[0290] Recall that the LB method defines a “symptom” as any patient dataitem that can affect diagnosis. Therefore, all of the mechanisms used bythe LB method to select, evaluate, and record the impact of symptoms areavailable and used to handle synergies. At script writing time, authorsdefine synergies and assign weights to them like any other symptom. Ifthe author intends to weight the, say, symmetry of onset and offset, theauthor defines a symptom and a question that will elicit the informationdirectly from the patient or indirectly from other data such as thevalue of other symptoms. At run time, the LB engine, whether in theHorizontal Axis of Inquiry or in the Vertical Axis of Inquiry, selectssymptoms, evaluates them, and—if applicable—adds the associated weightto them.

[0291] One feature of the LB method that bears noting, is that itdiagnoses disease by assigning “weights” to the patient's symptoms andthen uses the weight accumulated by a set of candidate diseases todetermine which disease(s) the patient most likely has. The basic weightassigned is for the mere presence or absence of a symptom. Now, underthe synergy concept in certain embodiments, there are two additionalways to analyze more detailed aspects of symptoms. First, for eachindividual symptom, the system can diagnose based on whether it is“first” for a given disease, and on the manner in which the symptomstarts, varies, and stops. Second, when several symptoms are present,the system can diagnose based on their presence as a combination, theirsequence and extent of overlap in time, and their relationship to (andchange in) the anatomic systems of the patient. In other words, thesesynergistic weights are “refinements” of the fundamental weighting. Theyspecify in detail the considerations for which the LB method can addextra diagnostic weights to a disease. This idea is referred to as“synergy weighting”; it reflects the fact that more detailed knowledgeabout one or more symptoms of a patient can be used to refine and focusthe diagnosis.

[0292] The following table lists several type examples of synergy thatcan be implemented. The examples are, of course, not exhaustive; theycan be extended to any special pattern of one or more symptoms occurringin a patient. Synergy Type This synergy adds weight to a disease if itdetects that . . . Symptom Presence patient has symptom A (the basicweighting concept) Symptom Level symptom A has specific value (PAINSEVERITY = 8 out of 10) Time-Based Synergy symptom A varies, cycles,pulsates, comes/goes, repeats Onset/Offset Slope symptom A starts orstops at a specified rate Onset/Offset Trend the rate of symptom A ischanging in a specified way Onset/Offset Symmetry symptom A starts andstops in a similar manner First Significant patient has the same FSS asdefined for the disease Symptom (FSS) Simultaneous patient has aspecified symptom set A, F, J, and R. Sequencing symptoms A and B occurin a specified sequence Overlap Synergy the length of time that symptomsA and B occur together Integral Synergy the area under the curve of, forexample, plotting the severity of a patient's pain over time

[0293] The following sections relate to describing and weightingspecific synergy types in certain embodiments of the invention.

[0294] 1. Symptom Presence Synergy

[0295] Symptom presence synergy assigns basic diagnostic weight to acandidate disease if a given symptom is present. At design time, theauthor can assign a weight to a symptom if it is present. For example,if the patient has a smoking history of ten pack-years, the diseaseEMPHYSEMA may get 50 points; if the patient recently went on a jungleexpedition, the disease MALARIA may get 50 points. At run time, thesystem determines if the symptom is present in the patient and assigns aweight to all diseases for which the symptom has been pre-defined with aweight.

[0296] Knowing the presence of symptoms, even without a value or timereference, can help to select candidate diseases for subsequentdiagnosis. Thus, a different set of candidate diseases can be selectedinitially for a patient complaining of COUGH than for a patientcomplaining of BACKPAIN.

[0297] 2. Symptom Level Synergy

[0298] Symptom level synergy assigns diagnostic weight based on a levelof a symptom present in a patient. At design time, the author definesseveral levels for a symptom, and weights for significant levels, suchas:

[0299] SEVERITY=0   0 points;

[0300] SEVERITY=1   10 points;

[0301] ·

[0302] ·

[0303] ·

[0304] SEVERITY=9   250 points;

[0305] SEVERITY=10   350 points.

[0306] At run time, the system determines: (1) if the symptom is presentand (2) at what level and, if so, (3) adds the corresponding weight tothe disease score.

[0307] The author can define symptom magnitude with any appropriateresolution. This is obviously very useful in describing diseases moreprecisely in terms of their symptom patterns.

[0308] 3. Time-Based Synergy

[0309] Time-Based Synergy is the ability of the LB method to analyze themanner in which a symptom varies over time in the patient, and to assignextra diagnostic weight to selected diseases based thereon. The way inwhich a symptom varies over time has great diagnostic significance. Oneexample is pain over time. The concept of using word spectra with aseries of graded adjectives has also been introduced, so that the wordsselected by the patient indicate varying degrees of symptom intensity.This synergy type includes the general ability to use various aspects ofa symptom time series to aid in, or refine a, diagnosis.

[0310] As described earlier, the symptom and valuator objects can beprogrammed with functions that compute (or ask the patient for) varioustime-based statistics such as onset, offset, slope, trend, curvature,area, etc. At run time, when a script requires a time-based statisticfor a given symptom, the symptom object invokes its valuator object tocompute them. Such computed values then become separate symptom valuesthat can be weighted and scored like any other symptom values. Usingthis synergy type, the symptom/valuator object becomes a smart processthat does not just store symptom values, but that can perform dynamic,intelligent, internal analysis of how the symptom behaved over time inthe patient, which generates useful diagnostic information in its ownright.

[0311] The script author can distinguish or differentiate amongcandidate diseases based on when a symptom occurs in the patient, or onhow the symptom varies over time in the patient. The author can use theactual symptom time variations to assign extra diagnostic weights to adisease.

[0312] One of the key features of the LB method is that it can use thetime when symptoms occur and change to help diagnose the patient. Thisoutperforms many other automated diagnostic methods.

[0313] 4. Onset/Offset Analysis Synergy

[0314] In one embodiment of the invention, onset/offset analysis synergyadds extra diagnostic weight to a disease if a given symptom exhibitsonset and/or offset in a specific manner. The type of onset and offsetof a symptom can convey great diagnostic information. The followingdescription is for onset analysis synergy; a similar description appliesto offset analysis synergy.

[0315] At design time, the script author specifies for each disease:

[0316] (1) that a given symptom's onset is to be synergy weighted,

[0317] (2) the onset types that can be used to select added synergyweight,

[0318] (3) the synergy weights to be added depending on the onset type.

[0319] At run time, in the phase where the system is adding synergyweights to candidate diseases, the system:

[0320] (1) detects that a given symptom's onset is to be synergyweighted,

[0321] (2) obtains the actual onset type for the symptom,

[0322] (3) compares the actual onset type to the pre-defined type,

[0323] (4) selects the onset synergy weight that corresponds to theactual type,

[0324] (5) adds the selected onset synergy weight to the disease score.

[0325] Two examples of this synergy are as follows: 1) the sinusoidalrelationship of severity of pain in colic, 2) the “stuttering” start ofunstable angina.

[0326] 5. Onset/Offset Slope Synergy

[0327] In one embodiment of the invention, onset/offset slope synergyadds extra diagnostic weight to a disease if a given symptom begins andrises to a maximum in a defined manner. The following description is foronset synergy; a similar description applies to the manner in which asymptom ends, or its offset.

[0328] At design time, the script author specifies for each disease:

[0329] (1) that a given symptom's onset is to be synergy weighted,

[0330] (2) the onset slope threshold(s) to be used for selecting thesynergy weight,

[0331] (3) the synergy weights to be added depending on the magnitude ofthe onset slope.

[0332] At run time, in the phase where the system is adding synergyweights to candidate diseases, the system:

[0333] (1) detects that a given symptom's onset is to be synergyweighted,

[0334] (2) obtains the actual onset slope for the symptom,

[0335] (3) compares the actual onset slope to the pre-defined slopethreshold(s),

[0336] (4) selects the onset synergy weight that corresponds to theactual slope,

[0337] (5) adds the selected onset synergy weight to the disease score.

[0338] The nature of the onset (and offset) of a symptom can conveygreat diagnostic information. For example, a headache that startssuddenly and is very severe has more chance of being a sub-arachnoidhemorrhage than a severe headache that comes on gradually. In vascularevents such as a myocardial infarction, the onset of pain is verysudden, that is, the slope of a line plotting severity versus time willbe nearly vertical. The sudden onset of vomiting and diarrhea in Staphfood poisoning contrasts to other causes of gastroenteritis and foodpoisoning.

[0339] 6. Onset/Offset Trend Synergy

[0340] In one embodiment of the invention, the onset (or offset) “trend”of a symptom refers to whether the symptom curve at that time point islinear or exponential, i.e., rising (or falling) at a constant rate orat an increasing or decreasing rate. This is referred to as “linear orexponential”. The following description is for onset trend synergy; asimilar description applies to the manner in which a symptom ends, orits offset.

[0341] At design time, the author specifies for each disease:

[0342] (1) that a given symptom's onset's curve trend is to be synergyweighted,

[0343] (2) the onset trend threshold(s) to be used for selecting thesynergy weight, (3) the synergy weights to be added depending on thetrend of the onset slope.

[0344] At run time, in the phase where the system is adding synergyweights to candidate diseases, the system:

[0345] (1) detects that a given symptom's onset trend is to be synergyweighted,

[0346] (2) obtains the actual onset trend for the symptom, (3) comparesthe actual onset trend to the pre-defined trend threshold(s),

[0347] (4) selects the onset synergy weight that corresponds to theactual trend,

[0348] (5) adds the selected onset synergy weight to the disease score.

[0349] The shape of the onset (and offset) curve of a symptom can conveydiagnostic information. In one embodiment, the diagnostic system uses aRunge-Kutta curve-fitting algorithm to identify the type of curve underconsideration. Other algorithms are used in other embodiments.

[0350] 7. Onset/Offset Symmetry Synergy

[0351] In one embodiment of the invention, onset/offset symmetry synergyassigns extra diagnostic weight if the onset and offset curves (or slopeif the relationship is linear) of a given symptom exhibit definedsymmetry characteristics.

[0352] At design time, the script author specifies for each disease:

[0353] (1) that a given symptom's onset and offsets are to be weightedfor symmetry,

[0354] (2) the parameters that define various symmetry relationships,

[0355] (3) the synergy weights to be added for a given symmetryrelationship.

[0356] At run time, in the phase where the system is adding synergyweights to candidate diseases, the system:

[0357] (1) detects that a given symptom's onset/offset symmetry is to besynergy weighted,

[0358] (2) obtains the actual onset and offset slopes and trends for thesymptom,

[0359] (3) converts the actual slopes and trends into pre-definedsymmetry parameters,

[0360] (4) selects the symmetry synergy weight that corresponds to theactual data,

[0361] (5) adds the selected weight to the disease score.

[0362] Onset and offset symmetry is important in making severaldiagnoses. For example, when a patient has a kidney stone that passesinto the ureter (the tube connecting the kidney to the bladder), thepatient experiences the sudden onset of very severe (and colicky) pain.In addition, when the stone passes into the bladder, the pain symptomfrequently disappears as suddenly as it came on.

[0363] 8. First Significant Symptom (FSS) Synergy

[0364] In one embodiment of the invention, first significant symptom(FSS) synergy assigns extra diagnostic weight to a disease if thepatient's FSS matches a list of possible FSSs for the disease. Thissynergy reflects the script author's real-world experience as to whichsymptoms tend to be the first ones noticed by a patient.

[0365] At scripting time, a disease script author creates a special listof symptoms that a patient might notice first, and associates a weightwith each symptom. For example, for Appendicitis: Anorexia 50 Nausea 30Epigastric Pain 10

[0366] At run time, if the patient reports that Nausea was the firstsymptom she or he noticed, the system will add 30 diagnostic points tothe diagnosis of Appendicitis (and similarly add some weight to allother diseases that show Nausea in their FSS tables).

[0367] The key is that we use the information that the patient has aspecific symptom first to advance those diagnoses that match thepatient. We already added points to the disease for just having thissymptom; now we add extra weight for it being first. That's the meaningof FSS synergy.

[0368] 9. Simultaneous Synergy

[0369] In one embodiment of the invention, simultaneous synergy assignsextra diagnostic weight to a candidate disease if two or more of itssymptoms are present in the patient over a given time period.

[0370] At design time, for each disease, the script author can defineany number of special symptom combinations as well as an associateddiagnostic weight that should be added to the disease score if thecombination is present. The disease author may use a Gantt chart toappreciate the simultaneous, sequential and overlapping synergies.

[0371] At run time, for each disease, the system:

[0372] (1) tracks the symptoms actually present in the patient,

[0373] (2) determines if any pre-defined symptom combination is present,and—if so—

[0374] (3) adds the associated weight to the score of the disease.

[0375] Simultaneous Synergy can be used very effectively by a scriptauthor to describe a disease in terms of syndrome, and to characterizehow various syndromes contribute to the disease.

[0376] 10. Sequencing Synergy

[0377] In one embodiment of the invention, sequencing synergy assignsextra diagnostic weight to a candidate disease if two or more of itssymptoms are present in the patient in a specific time sequence.

[0378] At design time, for each disease, the Script author may defineany number of special symptom sequences, with associated diagnosticweights to be added to the disease score if the patient exhibits thesymptoms in the specified order.

[0379] At run time, for each disease, the system:

[0380] (1) establishes an absolute start time for every symptom,

[0381] (2) tracks the symptoms actually present in the patient,

[0382] (3) detects if any author-defined sequence is present,

[0383] (4) determines whether the symptoms are present in thepre-defined time order,

[0384] (5) if appropriate, adds the sequence weight to the score of thedisease.

[0385] 11. Overlapping Synergy

[0386] In one embodiment of the invention, overlapping synergy assignsextra diagnostic weight to a candidate disease if two or more of itssymptoms are present in the patient at the same time for a specifiedamount of time.

[0387] At design time, for each disease, the author can define anynumber of special overlap symptom combinations, an overlap threshold,and a diagnostic weight that should be added to the disease score if thesymptom combinations overlapped in time for at least the specifiedthreshold time.

[0388] At run time, for each disease, the system:

[0389] (1) tracks the symptoms actually present in the patient,

[0390] (2) detects if any author-defined overlapping symptoms arepresent,

[0391] (3) computes for how long the symptoms overlapped in time,

[0392] (4) checks if the actual overlap meets or exceeds the specifiedoverlap threshold,

[0393] (5) if applicable, adds the specified overlap weight to the scoreof the disease.

[0394] 12. Integral (Area) Synergy

[0395] In one embodiment of the invention, integral synergy assignsextra diagnostic weight for the total amount of a symptom over aspecified time period. At design time, the author assigns diagnosticweight for the amount of a symptom that a patient has reported over atime interval. At run time, the system (1) tracks the time chart of thesymptom values, (2) computes the total symptom value (i.e., integratesthe symptom curve) between two time points, and (3) if appropriate, addsan area synergy weight to the disease score.

[0396] The amount of a symptom over time gives information regardingbiological and chemical body functions and reactions, which, in turn,have diagnostic value. An example is the amount of pain a patient hassuffered between two points in time. The integral synergy weight helpsthe system automatically recognize those patients that can benefit fromstrong analgesics, for example. In addition to diagnosis, this synergycould also be used for pain management. It could identify those patientswho perhaps cannot, or do not, identify themselves as needing narcoticanalgesics.

V. DESCRIPTIONS OF THE DRAWINGS

[0397] The software described by the following drawings is executed on astructure-based engine of a medical diagnostic and treatment advicesystem such as described in Applicant's U.S. Pat. No. 5,935,060. Oneembodiment of a structure-based engine is the list-based engine, butother embodiments may be implemented.

[0398] Referring now to FIG. 1, one embodiment of a diagnostic loopportion 100 of a medical diagnostic and treatment advice (MDATA) system,that may include a List-Based Engine (LBE), will be described in termsof its major processing functions. Note that treatment advice may beoptionally provided. However, the diagnostic aspect of this system isthe main focus of the invention. Each function is further described withan associated figure.

[0399] When the system starts, it assumes that another, off-line datapreparation program has prepared a suitable database of medicaldiagnostic data in the form of disease and symptom objects, DOs and SOsrespectively, and has assigned diagnostic weights to specific symptomvalues for each disease, and to special combinations or sequences ofsymptom values (called “synergies”). When a patient (who may access thesystem via a data communications network such as the Internet) presentsa medical complaint to the MDATA system to be diagnosed, the systemfirst retrieves all of the relevant disease objects from its databaseand assembles them into a candidate disease list. The system then usesthe diagnostic loop to develop a diagnostic profile of the candidatedisease list.

[0400] Inside the diagnostic loop, the system selects a current diseaseand symptom to pursue. Then the system obtains the value of the symptomfor the current patient, calculates the weights associated with thatvalue, and updates the scores of all affected candidate diseases withthe weights. The updated scores are then used to re-rank the diseasesand to select the disease and symptom to be evaluated in the nextiteration. In this manner, as the loop continues to iterate, the systembuilds up a diagnostic profile of the candidate diseases for the currentpatient. The loop can be interrupted at any point, and the then-currentdiagnostic profile examined in order to adjust the system parameters andcontinue the loop, or to terminate the loop, as desired. At the end ofthe loop, a diagnostic report is prepared that summarizes the actionstaken and the results computed.

[0401] The diagnostic loop 100 begins at state 102, where priorprocessing is assumed to have established a chief complaint for thecurrent patient and a diagnostic report needs to be determined. Movingto function 110, the system acquires the computer resources required forthe diagnostic loop. In this function, the system acquires computermemory as needed, creates required software objects, and sets variablesto their initial values based on the current options, limits, anddiagnostic goals. The system also creates a list of diseases that are tobe used as the initial candidates for diagnosis. Moving to function 120,the system selects one disease from the list of candidate diseases. Thisdisease becomes the current focus disease, i.e., the disease for which asymptom is to be evaluated. Moving to function 130, the system selectsone symptom from the list of symptoms associated with the currentdisease. This symptom becomes the current focus symptom to be evaluatedin the patient. Moving to function 140, the system evaluates the currentsymptom in the patient by suitable means such as questioning thepatient, using logical inferences, mathematical computations, tablelookups, or statistical analysis involving other symptom values. Movingto function 150, the system updates all candidate diseases that use thecurrent symptom with the new symptom value obtained in function 140.Moving to function 160, the system updates all working lists and recordswith new values, scores, and diagnoses. Moving to function 170, thesystem reviews the progress of the diagnosis to decide whether anotheriteration of the diagnostic loop is required. Proceeding to a decisionstate 172, the system tests whether the diagnostic loop is to beterminated e.g., by user direction. If not, the system moves to function120 for another iteration; otherwise, the system moves to function 180,where the system saves appropriate values computed in the diagnosticloop and destroys all temporary data structures and objects required forthe diagnostic loop. Continuing at state 182, the system returns areport of the diagnostic results.

[0402] Referring now to FIG. 2, the Set-up Diagnostic Loop function 110previously shown in FIG. 1 will be described. Function 110 acquires thecomputer resources and sets up the data structures needed for thediagnostic loop 100 (FIG. 1). The system is designed to be fullyadaptive to its environment, and must initialize various memorystructures to prepare for processing. In an object-based embodiment,this preparation includes the creation of various objects. Each objecthas an initialization function that allows the object to initializeitself as needed.

[0403] Function 110 begins with entry state 202, where a chief complaintand a diagnostic mode have been established by prior processing. Thechief complaint will be used in state 212 to retrieve associateddiseases. The diagnostic mode will be used throughout function 110 tocontrol detail processing. Moving to state 204, function 110 initializesthe HAI/VAI mode to either HAI or VAI, depending on the HAI/VAI modedesired for this operation of the diagnostic loop. In HAI mode, thesystem will consider all of the candidate diseases to select the nextfocus symptom; in VAI mode, the system will use only the list ofsymptoms of the current focus disease.

[0404] Moving to state 206, function 110 initializes the AlternativeSymptom mode to either permit or inhibit alternative symptom valuation,depending on the Alternative Symptom mode desired for this operation ofthe diagnostic loop. If alternative symptoms are permitted, the systemwill later accept alternative values in place of the specified symptomvalue. If alternative symptoms are inhibited, the system will laterinsist on evaluating the specified symptom. Moving to state 208,function 110 initializes other internal variables that support the loopprocessing such as control flags, option indicators, loop limits, andloop goals. The exact variable and value of each variable depends on theparticular code embodiment chosen for the computer program. Moving tostate 210, function 110 obtains and initializes special computerresources required for this operation of the diagnostic loop. Thedetails of this initialization depend on the code embodiment chosen forthe computer program. For example, if an object is used to represent thelist of candidate diseases, state 210 creates and initializes an emptycandidate disease object; but if a relational table is used to representthe list of candidate diseases, state 210 creates and initializes anempty table to contain the candidate diseases. Moving to state 212,function 110 retrieves from the disease database all those diseases thatexhibit the chief complaint being diagnosed and (if available) thepatient's symptom time profile (FIG. 28).

[0405] As part of the off-line data gathering and preparation process,every disease in the disease object database is associated with at leastone chief complaint and with a time profile of symptoms. Thisassociation is used here to retrieve the list of diseases thatconstitute the initial candidate set. By “candidate” disease is meantany human disease, as yet unidentified, that has some probability ofbeing the patient's illness, based on the symptoms and the chiefcomplaint indicated by the patient. Moving to a function 220, variousinternal working structures required to efficiently perform suchdetailed processing as sorting, searching, and selecting subsets ofdiseases and symptoms are initialized. Function 220 is further describedin conjunction with FIG. 3. Moving to state 222, function 110 returnscontrol to the process that called it, in effect moving to function 120of FIG. 1.

[0406] Referring now to FIG. 3, the Set-up Disease-Symptom Structurefunction 220, which sets up the candidate disease list and actualsymptom data structures to be used in the diagnostic loop, will bedescribed. The candidate disease list is generated and then partitionedinto three sublists of urgent, serious, and common diseases. Thisseparation allows the system to consider candidate diseases in order ofurgency and seriousness before it considers the common diseases.

[0407] Function 220 begins with an entry state 302. Moving to state 304,function 220 creates a disease-symptom matrix (DSM), which is a datastructure with columns for all the diseases selected by the chiefcomplaint, rows for the maximum number of symptoms used by all candidatediseases, and time slices (Z-axis) for the time intervals used by thediseases. Other disease-symptom structures may be used in otherembodiments. Moving to state 306, function 220 extracts all diseasesmarked ‘urgent’ from the candidate diseases and sorts these bydecreasing urgency. Moving to state 308, function 220 places the mosturgent disease as the leftmost column of a Disease-Symptom Matrix (DSM)[which can be considered one time slice of a Disease-Symptom Cube(DSC)]. Moving to state 310, function 220 places the remaining urgentdiseases in the next columns of the DSM. Moving to state 312, function220 extracts all diseases marked ‘serious’ from the candidate diseaselist; sorts these serious diseases by decreasing seriousness; places themost serious disease as the next available leftmost column of the DSM,next to the urgent diseases; and places the remaining serious diseasesinto the next columns of the DSM. Moving to state 314, function 220sorts the candidate diseases that remain after the urgent and seriousdiseases were removed by decreasing prevalence, i.e., the probability ofoccurrence of the disease in the population from which the patientcomes, and places the remaining diseases in order of decreasingprevalence as the next available leftmost column of the DSM, next to theserious diseases. Moving to state 316, function 220 returns control tothe process that called it, in effect to state 222 of FIG. 2.

[0408] Referring now to FIG. 4, the Pick Current Disease function 120,in which the candidate disease list is searched to select one disease asthe current focus disease, will be described. The selection criteria canbe any computation that can identify diseases with high potential forbeing the actual disease of the patient, such as selecting diseasesbased on their performance so far in the current diagnostic session, onhigh diagnostic score, high rate of change of score (diagnosticmomentum), number of positive responses to questions, or beststatistical match of disease timelines, which may happen in the HAImode. In another selection mode (VAI), an external user or process hasalready selected the focus disease, so that the system has no choice.

[0409] Function 120 begins with the entry state 402, where a list ofdiseases exists that are candidates for diagnosis. Function 120 selectsone of these candidate diseases to become the current focus disease. Theselection can be made using one of many rules; which rule is useddepends on the diagnostic mode. FIG. 4 shows two rules being tested, butany number of rules can be added. Moving to a decision state 404,function 120 first checks whether the candidate disease list is empty.If so, there are no more diseases to examine, and function 120 moves tostate 440. At state 440, function 120 sets an outcome signal to indicatethat no current disease has been selected and then moves to state 434,where the process returns. If the candidate list is not empty atdecision state 404, then there is at least one candidate diseaseremaining and function 120 moves to a decision state 406 to test whetherall candidate diseases have been processed. If so, function 120 moves tostate 442, where it sets an outcome signal to that effect and moves tostate 434 to return control. However, if, at decision state 406, somediseases remain to be processed, function 120 moves to a decision state408. At decision state 408, if the selection mode is set to VAI, i.e.,forced to use a specific disease, function 120 moves to a decision state410, otherwise to state 412. At state 410, if the VAI disease has notyet been diagnosed, function 120 moves to state 430, selects the VAIdisease as the current disease and moves to state 432. But if, atdecision state 410, if the disease that was pre-selected has alreadybeen diagnosed, function 120 moves to state 412, to reset processing tothe HAI mode.

[0410] With state 414 begin the states of actually selecting a disease.The diagnostic mode that is in effect when the diagnostic loop startsspecifies or implies a disease selection rule or criterion. This rule isbased on either the actual diagnostic progress so far, or the potentialprogress that could be made, using such diagnostic measures asweighting, momentum, score, and probability. This selection rule can bechanged by internal processing or external request, but some selectionrule will always be in effect. Function 120 uses the rule to select oneof the candidate diseases as focus disease. Moving to a decision state414, if the selection rule is to select the candidate disease with thehighest actual diagnostic momentum, function 120 continues to state 416.At state 416, function 120 selects the candidate disease with thehighest current diagnostic momentum and then moves to state 432. But if,at decision state 414, the current rule is otherwise, function 120 movesto a decision state 418.

[0411] At decision state 418, if the selection criterion is to selectthe candidate disease with the highest potential diagnostic momentum,function 120 moves to state 420, selects the candidate disease with thehighest potential diagnostic momentum and then moves to state 432. Butif, at decision state 418, the current diagnostic mode is otherwise,function 120 moves to a decision state 422. At decision state 422, ifthe diagnostic mode is to select the candidate disease using some othercriterion such as time profile matching or direct patient input,function 120 moves to state 424, which uses some other criterion in asimilar manner to select the disease and then moves to state 432. Butif, at decision state 422, there are no more criteria to be used,function 120 moves to state 426, applies the default rule that maysimply select the next eligible candidate disease and then moves tostate 432. At state 432, function 120 sets an outcome signal to indicatethat a current focus disease has been selected and then function movesto state 434 to return the outcome signal and the current diseaseidentifier to the process that called function 120, in effect tofunction 130 of FIG. 1.

[0412] Referring now to FIG. 5, the Pick Current Symptom function 130,which selects a symptom of the current focus disease to become the nextcurrent focus symptom, will be described. Here, the system examines thelist of symptoms of the current focus disease and uses various criteriato select one of them as the next focus symptom. The goal is to select asymptom that will advance the diagnostic score with the least system orpatient effort, which can be achieved in a number of ways such as byselecting a symptom for which the value has already been obtained byanother disease, or a symptom that has been specially identified by theauthor, or a symptom with high diagnostic weight, or one that is likelyto rule the disease in or out at once, or symptoms that have highcommonality across diseases. Once function 130 has selected a symptom inthis manner, it will also select all symptoms that have been identifiedas acceptable alternatives by the author.

[0413] Function 130 begins with the entry state 502, where a currentfocus disease has been selected and the function must now select fromthat disease's symptom list a current focus symptom, plus possibly oneor more alternative symptoms, if any were specified by the author. Thesymptom can be selected using one of many rules. Which rule is useddepends on the diagnostic mode. Moving to a decision state 504, function130 first checks whether there are any symptoms remaining in the currentdisease that have not yet been evaluated for the current patient. If so,function 130 moves to state 506, which returns an outcome signal thatindicates that no current symptom has been selected. But if, at decisionstate 504, there is at least one eligible symptom, function 130 moves toa decision state 508.

[0414] Decision state 508 begins the actual selection of a symptom. Thediagnostic mode that is in effect when the diagnostic loop starts canspecify or imply one of many symptom selection rules or criteria, whichcan be changed by internal processing or external request. However, inone embodiment, some symptom selection rule will always be in effect. Inaddition, for any given symptom, a disease can identify one or morealternative symptoms that can be used in its place. The focus symptomreturned by state 130 can thus consist of a symptom package thatcontains at least one symptom plus zero or more alternative symptoms. Atdecision state 508, function 130 tests whether the disease has symptomsthat must be evaluated before other symptoms of that disease. Suchsymptoms serve to quickly eliminate a disease that does not meet basiccriteria. If the disease has such initializing symptoms, function 130moves to a decision state 510. At decision state 510, if all of theinitial symptoms have been evaluated, function 130 moves to a decisionstate 516. But if, at decision state 510, there are any unevaluatedinitial symptoms, function 130 moves to state 512, selects the nextinitial symptom as focus symptom and moves to return state 514. Movingto decision state 516, if the current diagnostic mode specifiesselecting symptoms with the largest diagnostic weight, function 130moves to state 518, selects the symptom with the largest diagnosticweight and moves to return state 514. But if, at decision state 516, therule is not to select the symptom with the largest weight, function 130moves to a decision state 520. At decision state 520, function 130accommodates symptoms that are related to each other in some way such asgroups, combinations, or sequences. If the current diagnostic modeindicates that related symptoms are to be considered, function 130 movesto state 522, but if related symptoms are not to be considered, function130 moves to state 526.

[0415] At decision state 522, if there is a symptom related to thepreviously evaluated symptom, function 130 moves to state 524. At state524, function 130 selects a symptom that is related to the previouslyevaluated symptom and moves to return state 514. But if, at decisionstate 522, there is no related symptom, function 130 moves to state 526.At decision state 526, if the current diagnostic mode indicates thatsymptoms are to be considered that are easiest to evaluate, function 130moves to state 528, selects the next symptom that is easiest to evaluateand then moves to return state 514. But if, at decision state 526, therule is not to select the easiest symptom, function 130 moves to adecision state 530. At decision state 530, if the current diagnosticmode indicates that symptoms are to be selected at random, function 130moves to state 532, selects the next symptom at random from the currentdisease's symptom list and then moves to return state 514. But if, atdecision state 530, the rule is not to select a random symptom, function130 moves to state 534, selects the next eligible symptom from thecurrent disease's symptom list and then moves to return state 514. Atstate 514, function 130 returns the current focus symptom (and allalternative symptoms, if any) to the process that called function 130,in effect to function 140 of FIG. 1.

[0416] Referring now to FIG. 6, the Obtain Symptom Value function 140,which evaluates the current focus symptom, i.e., establishes a specificvalue that occurred or existed at some time t in the patient, will bedescribed. At this point in the diagnostic loop, the system has selecteda current focus symptom and must now determine its value at some time tin the on-line patient. Symptom values can be simple (e.g., patient is asmoker) or detailed (e.g., patient has a 12-year, 2-packs/day smokinghistory); values can be simple numbers or symbols, or complex graphics,photos, or disease timelines (see, for example, FIG. 28).

[0417] Function 140 must now select either the current symptom or one ofits alternatives, and then obtain its value in the patient at specifictimes. If the current diagnostic mode permits the use of alternativesymptoms, and the current focus symptom has an alternative symptom thathas already been evaluated, then the value of that alternative symptomis used at once, without further evaluation. The time saved by thisevaluation shortcut is the basic reason for the use of alternativesymptoms.

[0418] How the value itself is obtained depends on the symptom beingevaluated and can use many different methods such as reviewing thepatient's medical record, asking the on-line patient direct questions,drawing logical inferences from other symptom values, using mathematicaland statistical formulas, using specially prepared lookup tables, evenhaving the patient perform self-examinations. These different evaluationtechniques are described here collectively as a ‘valuator function’.

[0419] Function 140 begins with the entry state 602, where a currentfocus disease and symptom have been selected by prior processing. Movingto a decision state 604, function 140 first checks whether an acceptablevalue was already obtained during this session, perhaps by some otherdisease or some acceptable alternative symptom. If the current focussymptom already has a value, function 140 moves to state 606 to returnthat value; otherwise, function 140 moves to a decision state 608. Atdecision state 608, function 140 checks whether the current symptomalready has a value in the patient's medical record. If so, function 140moves to state 606 to return that value; otherwise, function 140 movesto a decision state 609. At decision state 609, if the current symptomhas alternative symptoms and the mode permits their use, function 140moves to a decision state 610; otherwise function 140 moves to adecision state 612. At decision state 610, if the current symptom has anacceptable alternative symptom value then function 140 skips furtherevaluation and returns the alternative value at time t from state 611 atonce at state 606; otherwise function 140 moves to decision state 612.

[0420] At decision 612, function 140 begins the process of evaluatingthe current symptom by determining the valuator type of the currentsymptom. If the valuator type is a direct question, function 140 movesto function 620 to ask questions of the on-line patient, which isdescribed in FIG. 7 and then moves to state 606. But if, at decisionstate 612, the valuator type is a mathematical formula, function 140moves to function 630 for evaluating the formula (described in FIG. 8)and then moves to state 606. But if, at decision state 612, the valuatortype is a table lookup, function 140 moves to function 640 to look upthe value in a table (described in FIG. 9), and then moves to state 606.But if, at decision state 612, the symptom value is based on analyzing aspectrum of terms, function 140 moves to function 650 to perform thespectrum of terms analysis and obtain a value, which is described inFIG. 9. Then function 140 moves to state 606. Finally, if at decisionstate 612 the valuator is of some other type, such as disease timeprofile matching (FIG. 28), function 140 moves to state 660 to invokethat valuator and obtain a value in a similar manner. Then function 140moves to state 606 to return the current symptom and its value at sometime t to the calling process that invoked function 140, in effect tofunction 150 of FIG. 1.

[0421] Referring now to FIG. 7, the Question Valuator function 620,which is part of a question valuator object, will be described. Aquestion valuator object obtains a symptom value at time t by asking anon-line patient one or more questions. To ask the questions, it uses oneor more node objects that are pre-programmed by the script author tocommunicate with the patient in some natural language, using appropriateinstructions, definitions, explanations, questions, and responsechoices. The response selected by the patient is encoded as a symptomvalue and ultimately returned to the caller of function 620.

[0422] Function 620 begins with entry state 702, where the current focusdisease, symptom, and question objects have been established by priorprocessing and are given to the function. Function 620 now questions theon-line patient to obtain a value for the symptom at some time t. Movingto state 706, function 620 retrieves, from a database of node objects, aset of nodes listed in the current valuator object. Moving to state 708,function 620 displays the next node, which includes instructions and aquestion, to the patient. Moving to a decision state 710, if there is noresponse within a prescribed time, function 620 moves to return state712 and returns a signal that the question object has timed out. But if,at decision state 710, function 620 obtains a response from the patient,the function moves to a decision state 714. At decision state 714,function 620 determines whether the response is a final symptom value ora signal to activate another node. If the response activates anothernode, function 620 moves back to state 708, which repeats the sequenceof question and response states with a different node. Over time, theeffect of this sequence is to generate a dialog with the patient thatculminates in a symptom value being generated at state 714. When, atdecision state 714, the patient's response is a value, function 620moves to state 716 and returns the value obtained from the patient.

[0423] Referring now to FIG. 8a, the Formula Valuator function 630,which is part of a formula valuator object, will be described. A formulavaluator object computes the value of a symptom at some time t byevaluating a given formula. In the object-based embodiment, each formulais embedded in a different formula valuator object, and there are asmany different formula valuator objects in the system as there areformulas. Any other object that needs to evaluate a formula can call theappropriate formula valuator. A simple example is to convert an absolutedate such as Dec. 7, 1941 into an age in years at some later time t.

[0424] Function 630 begins with entry state 802, where current focusdisease, symptom, and valuator objects have been selected by priorprocessing. Function 630 now evaluates the formula to obtain a value forthe symptom. Moving to function 810, the formula calculator object isinvoked. Moving to state 812, function 630 returns the computed value.

[0425] Referring now to FIG. 8b, the Execute Formula Valuator function810, which evaluates a given formula that may use other symptom valuesto compute a symptom value at time t. The formula is given to function810 as a set of suitably encoded operations and operands in someformalized system of mathematics such as arithmetic, geometry,trigonometry, algebra, calculus, probability, or statistics. Function810 performs the required operations and returns the computed value. Onespecial sub-processing case occurs when an operand of the formula isitself a symptom that still needs to be evaluated, in which casefunction 810 interrupts the evaluation, causes the operand symptom to beevaluated, and then continues the evaluation of the formula. This is apotentially recursive process, since the evaluation of a symptom canitself involve the evaluation of a different formula. By using thisdesign, any structure of nested formulas, using any symptom objects canbe specified by a script author and evaluated when needed.

[0426] Function 810 begins with the entry state 820, where the formulaand its arguments are known. Moving to state 822, the formula isevaluated as far as is possible with the given arguments. In some cases,this will completely evaluate the formula; in others, it will encounteran argument that is itself a symptom that still needs to be evaluated.Moving to a decision state 824, if an argument requires furtherevaluation, function 810 moves to the Execute Symptom Object function140; otherwise it moves to state 832. At function 140, the argumentsymptom is evaluated by calling the appropriate symptom object (FIG. 6);then function 810 moves back to state 822 to continue evaluating theformula. At state 832, function 810 continues to evaluate the formulauntil the final value has been computed. Moving to state 834, the finalvalue is returned.

[0427] Referring now to FIG. 9, the Lookup Valuator function 640, whichis part of a lookup valuator object, will be described. A lookupvaluator object computes the value at time t of a symptom by looking itup in a table or chart. It is often the case that the value of a symptomis known to the system indirectly, perhaps from some other context inwhich a chart or table was prepared. One simple example is a time-basedtemperature chart that can be used to retrieve the value of fever attime t. Alternatively, function 640 could use statistical computationsbased on a chart, such as counting certain occurrences, finding the areaunder a disease timeline between two given times, or matching diseasetimelines.

[0428] Function 640 begins with entry state 902, where a symptom hasbeen selected for evaluation. Moving to state 904, the symptom is lookedup in a prepared lookup medium such as a graph, chart, or databasetable. Moving to state 906, function 640 returns the value to theprocess that called function 640.

[0429] Referring now to FIG. 10, the Spectrum of Terms Valuator function650, which is used for symptoms that depend on the patient's subjectivedescription, will be described. Function 650 converts a patient'ssubjective description of a symptom at time t into a specially encodedtoken that is returned by the function and processed by the system likeany other value. Function 650 provides a list of key descriptor words tothe patient, lets the patient select one or more words, and encodes theselected words into a token that is returned.

[0430] States 1004 and 1006 of the figure show that the spectrum ofdescriptor terms is prepared, and weights are assigned to the variousterms in an off-line preparation process of the symptom object. Thesedata are typically prepared and stored in a database that is accessedduring on-line diagnosis.

[0431] In the on-line diagnostic system, function 650 begins at state1008, where the function presents the spectrum of terms to the patientin some manner that allows the patient to select a set of descriptiveterms for time t. Moving to state 1010, function 650 obtains andprocesses the tern(s) selected by the patient. Moving to a decisionstate 1014, if other aspects of the symptom are to be processed,function 650 moves to state 1016; otherwise the function moves to state1018. At state 1016, function 650 prepares to process the next aspect ofthe symptom's term spectrum and then moves back to state 1008. At state1018, function 650 collects and saves the terms collected for time tinto a suitable code that can be returned as a value. Moving to state1020, function 650 encodes the terms collected from the patient andreturns them as the value to the process that called function 650.

[0432] Referring now to FIG. 11, the Apply Symptom Value function 150,which receives a symptom value that has just been elicited or computedand applies it and its effects to various candidate diseases, will bedescribed. In one embodiment, using a central system, function 150 loopsthrough the candidate disease list and applies to each disease D theeffects of the new value. For each disease D, it retrieves theappropriate diagnostic weights, computes the applicable synergisticweights, computes the applicable synergy weights, and notes any othereffects mandated by the disease author, such as mandatory score changes,disease status changes due to rule-ins and rule-outs, and changesrequired to handle value changes of symptoms that are acting asalternative symptoms in other diseases.

[0433] In an alternative, object-based embodiment, each disease objecthas a built-in method that processes new symptom values; and function150 calls that method to “notify” each disease object of the new symptomvalue. Each disease object is programmed to apply the effects of the newvalue to its own data, which simplifies the handling of certain highlydisease-specific updating rules. However, in either embodiment, the samelogical processing takes place.

[0434] Note, in one embodiment, that the diagnostic weight changescomputed as a result of the new value are merely saved, but are notadded to the diagnostic scores until after all candidate disease changeshave been computed and all diseases can be updated in unison (see FIG.21). This is necessary to prevent changes in scores, rulings,alternative symptom weights, and synergistic effects of diseases nearthe beginning of the candidate list from influencing and distortingcomputations for diseases further down the list. Computing score changesand incrementing scores is a two-pass process to insure correctadvancing of all diseases scores as a single generation.

[0435] Function 150 begins with entry state 1102, where a new symptomvalue has been computed and must be applied to all candidate diseasesthat use this symptom. Moving to state 1104, function 150 initiates aloop that processes every disease D in the candidate disease list.Moving to a decision state 1106, if disease D does not use the newsymptom, function 150 ignores disease D and moves to a decision state1122 for another iteration of the loop. But if, at decision state 1106,disease D does use the current symptom, function 150 moves to state 1108to process it. At state 1108, function 150 retrieves, from the weightstable for disease D, the diagnostic weight specified for the value ofthe current symptom at time t. This new weight is stored in the diseaseobject for D for later processing.

[0436] Proceeding to state 1110, if the diagnostic weighting for thecurrent symptom depends on analyzing incremental changes in symptomvalues over a time interval, function 150 computes the effect of thechanges, retrieves a corresponding weight, and saves it for laterupdating. Advancing to a Compute Synergies function 1120, the impact ofthe new symptom value on disease D is computed, as further described inconjunction with FIG. 12. Moving to decision state 1122, function 150checks if there are more candidate diseases to be processed. If so,function 150 moves to state 1124, increments the loop index D and movesback to state 1106 to start another iteration of the loop. But if, atdecision state 1122, all candidate diseases have been processed,function 150 moves to state 1126 and returns to the calling process.

[0437] Referring now to FIG. 12, the Compute Synergies function 1120(used in FIG. 11), which computes the synergistic weight, if any, of agiven symptom value, will be described. Synergy here refers to specialpredefined qualities of symptoms as well as time-based relationships andinteractions among different symptoms, which often have a significantdiagnostic impact. Whereas the use of diagnostic weights for simplesymptom values is a first-order effect, the use of time-basedsynergistic values is a second-order effect, a mathematical“fine-tuning” that helps to differentiate competing diagnoses anddistinguishes the MDATA system from other automated diagnostic systems.Note that only a few of the major synergy types are shown; there aremany possible synergy types that can all be analyzed in a similar manneras shown here.

[0438] Function 1120 begins with entry state 1202, where a new symptomvalue has been computed and must now be applied to all synergisticsymptoms for a given disease. Moving to a decision state 1204, function1120 tests whether the given disease has any symptoms that involvesynergies. If not, function 1120 returns at state 1206; otherwise,function 1120 moves to state 1208 and initializes a loop that processesevery synergy i defined for the given disease. Continuing at a decisionstate 1210, function 1120 obtains the next synergy i of the disease andreviews its type. Depending on the type of synergy i, function 1120computes the synergy as follows:

[0439] If the type of synergy i is First Significant Synergy, function1120 moves to function 1220 to compute the FSS Synergy, which is furtherdescribed in conjunction with FIG. 13. Then function 1120 moves to adecision state 1272. If, at decision state 1210, the synergy type isOnset or Offset Synergy, function 1120 moves to function 1230 to computethe Onset or Offset Synergy, which is further described in conjunctionwith FIG. 14. Then function 1120 moves to decision state 1272. If, atdecision state 1210, the synergy type is Sequencing Synergy, function1120 moves to function 1240 to compute the Sequencing Synergy, which isfurther described in conjunction with FIG. 18. Then function 1120 movesto decision state 1272. If, at decision state 1210, the synergy type isSimultaneous Synergy, function 1120 moves to function 1250 to computethe Simultaneous Synergy, which is further described in conjunction withFIG. 19. Then function 1120 moves to decision state 1272. If, atdecision state 1210, the synergy type is Time Profile Synergy, function1120 moves to function 1260 to compute the Time Profile Synergy, whichis further described in conjunction with FIG. 20. Then function 1120moves to decision state 1272. If, at decision state 1210, the synergytype is some other synergy, function 1120 moves to state 1270 to computethe synergy. State 1270 is intended to show that there may be many othersymptom combinations that exhibit synergy, which would be computed inlike manner as functions 1220 through 1260. After any one synergysymptom has been computed, function 1120 moves to decision state 1272and checks whether there are more synergies to process. If so, function1120 moves to state 1274 where it increments the index that selects thenext synergy and then moves back to state 1210 to initiate anotheriteration of the loop. But if, at decision state 1272, there are no moresynergies to compute, function 1120 moves to state 1276 and returns tothe calling process.

[0440] Referring now to FIG. 13, the Calculate First Significant Symptom(FSS) function 1220 (FIG. 12) will be described. The FSS function 1220determines if a given actual symptom value belongs to the firstsignificant symptom of a given disease in order to add extra diagnosticweight to that disease. The meaning of “first significant symptom” isillustrated in and described in conjunction with FIG. 28 as the earliestsymptom in the disease process.

[0441] Function 1220 begins with entry state 1302, where a new value hasbeen computed for a symptom and function 1220 must now retrieve theextra diagnostic weight associated with the symptom being the FirstSignificant Symptom of the disease. Moving to a decision state 1304,function 1220 tests whether the given disease identifies any firstsignificant symptoms. If not, function 1220 returns at once at state1306, otherwise function 1220 moves to a decision state 1308 and checkswhether the given symptom is identified as a First Significant Symptomof the given disease's timeline. If not, function 1220 returns at onceat state 1306, otherwise function 1220 moves to state 1310. At state1310, function 1220 retrieves the diagnostic weight specified for thegiven symptom value for the disease and moves to state 1312. At state1312, function 1220 saves the diagnostic weight, moves to state 1306 andreturns to function 1120 (FIG. 12).

[0442] Referring now to FIG. 14, the Calculate Onset [Offset] Synergyfunction 1230 (used in FIG. 12) will be described. Function 1230analyzes if and how the values of a given symptom exhibit special onset(or offset) characteristics that have medical significance and thus addextra diagnostic weight to the disease.

[0443] Function 1230 begins with entry state 1402, where a new value hasbeen computed for a symptom and the function must now retrieve the extradiagnostic weight associated with special onset and offset values of thegiven symptom. Moving to a decision state 1404, function 1230 testswhether the disease uses onset/offset analysis and whether it identifiesthe given symptom value as a special onset or offset value. If the givensymptom does not use onset/offset analysis, function 1230 moves to state1416 to return at once (at state 1416); otherwise it moves to function1410 a. At function 1410 a, the onset or offset synergy of the newsymptom value is analyzed (as described at FIG. 15) and then moves to adecision state 1414. At decision state 1414, function 1230 checkswhether its previous processing has changed any onset or offset values.If so, the function moves to function 1410 b, or otherwise to state1416. At function 1410 b, the symmetry between onset and offset isanalyzed and synergy weight is assigned (as described at FIG. 15). Atthe completion of function 1410 b, function 1230 moves to state 1416 andreturns to the calling function 1120 (FIG. 12).

[0444] Referring now to FIG. 15, function 1410, which analyzes the onsetand offset values of a given symptom and determines theircharacteristics with respect to time, will be described. Function 1410includes a portion 1410 a to analyze an onset or offset synergy and aportion 1410 b to analyze symmetry synergy.

[0445] Function 1410 begins with entry state 1502, where the new symptomvalues are given. Moving to a decision state 1504, if the given valuesare not related to onset or offset, the function returns a “no data”signal at decision state 1504; otherwise function 1410 can performportion 1410 a to analyze an onset or offset synergy and moves to adecision state 1508. At decision state 1508, if there are new valuesrelated to symptom onset, function 1410 moves to function 1510, orotherwise to a decision state 1522. At function 1510, the diagnosticweight of the slope of symptom onset with respect to time is computed asfurther described in FIG. 16. Moving to function 1520, the diagnosticweight of the trend, i.e., the change of slope of symptom onset withrespect to time is computed, as further described in FIG. 17. Moving todecision state 1522, if there are new offset values, function 1410 movesto function 1510′, otherwise function 1410 returns at state 1528. Atfunction 1510′, diagnostic weight of the slope of symptom offset withrespect to time is computed, as further described in FIG. 16. Moving tofunction 1520′, the diagnostic weight of the trend, i.e., the change ofslope of symptom offset with respect to time is computed, as furtherdescribed in FIG. 17. Moving to a decision state 1524, if there are bothonset and offset values in the symptom, function 1410 can performportion 1410 b to analyze symmetry synergy and proceeds to state 1526;otherwise function 1410 returns at state 1528. At state 1526, thefunction portion 1410 b computes the diagnostic weight of the symptomonset/offset symmetry. Moving to state 1528, function 1410 returns tothe calling function 1230 (FIG. 14).

[0446] Referring now to FIG. 16, function 1510, which computes thediagnostic weight of onset and offset slopes for a symptom, will bedescribed. This description is for symptom onset (1510); a similardescription applies for symptom offset (1510′). Function 1510 beginswith entry state 1602, where new symptom values are given. Moving to afunction 2500, the slope with respect to time of the given onset oroffset value is computed, as further described in conjunction with FIG.25. Moving to a decision state 1606, if no valid slope was returned byfunction 2500, function 1510 returns at state 1614; otherwise it movesto a decision state 1608. At decision state 1608, if the onset slopedoes not reach or exceed the onset slope threshold, function 1510returns at state 1614; otherwise function 1510 moves to state 1610. Atstate 1610, function 1510 retrieves the weight assigned to the onsetslope value for the given symptom and saves it for later analysis in thedisease object. Moving to state 1614, function 1510 returns to function1410 (FIG. 15).

[0447] Referring now to FIG. 17, function 1520, which computes thediagnostic weight of onset (1520) and offset (1520′) trends for asymptom, will be described. The speed with which a symptom begins orends in a patient has diagnostic significance, which is determined andweighted by this function. This description is for symptom onset trend(1520); a similar description applies for symptom offset trend (1520′).Function 1520 begins with entry state 1702, where new symptom values aregiven. Moving to a function 2600, the trend with respect to time of thegiven onset value is computed, as further described in conjunction withFIG. 26. Moving to a decision state 1706, if no valid trend was returnedby function 2600, function 1520 returns at state 1714; otherwise itmoves to a decision state 1708. At decision state 1708, if the onsettrend is less than the onset trend threshold, function 1520 returns atstate 1714; otherwise function 1520 moves to state 1710. At state 1710,function 1520 retrieves the weight assigned to the onset trend value andsaves it for later analysis in the disease object. Moving to state 1714,function 1520 returns to the caller function 1410 (FIG. 15).

[0448] Referring now to FIG. 18, the Calculate Sequence Synergy function1240 (FIG. 12) will be described. Function 1240 checks whether aspecific, author-defined sequence of symptom values occurred in thepatient being diagnosed for a given disease. If so, function 1240retrieves the extra diagnostic synergy weight associated with thatspecial symptom sequence and saves it for later analysis.

[0449] Function 1240 begins with entry state 1802, where a disease and asymptom value at some time t is given to the function. Moving to adecision state 1804, function 1240 tests whether the disease authordefined any sequence synergy weighting at all. If so, function 1240moves to state 1806, otherwise it returns at once at return state 1814.At state 1806, function 1240 retrieves from the disease object allsymptom values that are involved in the sequence synergy computation.Moving to state 1808, function 1240 compares the time sequence ofsymptom values actually reported in the patient to the author's timesequence of symptoms. Moving to a decision state 1810, if the patient'ssymptom sequence matches the author's, function 1240 moves to state1812; otherwise the function returns at state 1814. At state 1812,function 1240 retrieves the diagnostic weight associated with thesymptom time sequence from the given disease's weight table and savesthe weight with any other actual weights of the given disease. Moving tostate 1814, function 1240 returns to its caller function 1120 (FIG. 12).

[0450] Referring now to FIG. 19, the Calculate Simultaneous Synergyfunction 1250 (FIG. 12) will be described. Function 1250 checks whethera specific, author-defined set of symptom values occurred at the sametime or over the same time period in the patient being diagnosed for agiven disease. If so, function 1250 retrieves the extra diagnosticweight associated with that special set of simultaneous symptoms andadds it to the list of actual diagnostic weights of the disease.

[0451] Function 1250 begins with entry state 1902, where a disease and asymptom value at some time t is given to the function. Moving to adecision state 1904, function 1250 tests whether the disease authordefined any simultaneous synergy weighting at all. If so, function 1250moves to state 1906, otherwise it returns at once at return state 1912.At state 1906, function 1250 retrieves from the disease object allsymptom values that are involved in the simultaneous synergycomputation. Moving to a decision state 1908, if the patient's symptomset at time t matches the author's pre-defined symptom set, function1250 moves to state 1910; otherwise the function returns at state 1912.At state 1910, function 1250 retrieves the diagnostic weight associatedwith the simultaneous symptom set from the given disease's weight tableand saves the weight with any other actual weights of the given disease.Moving to state 1912, function 1250 returns to its caller function 1120(FIG. 12).

[0452] Referring now to FIG. 20, the Calculate Timeline Profile Synergyfunction 1260 (FIG. 12) will be described. Function 1260 checks whetherthe patient being diagnosed for a given disease has reported a symptomtime profile (FIG. 28) or individual symptom values occurring at time tsuch that they match or “fit into” a specific, author-defined diseasetime profile or disease timeline. If the given symptom occurred in thepatient with predefined values at times that correspond to a diseasetime profile defined by the author, function 1260 retrieves the extradiagnostic weight that the author associated with the symptom timeprofile and adds it to the list of actual diagnostic weights of thedisease.

[0453] Function 1260 begins with entry state 2002, where a disease and asymptom value at time t is given to the function. Moving to a decisionstate 2004, function 1260 tests whether the disease author defined anysymptom time profile weights at all. If so, function 1260 moves to state2006, otherwise it returns at once at return state 2012. At state 2006,function 1260 retrieves from the disease object the symptom time profilethat is involved in the computation. Moving to a decision state 2008, ifthe patient's symptom time profile matches the author-defined timeprofile, function 1260 moves to state 2010; otherwise the functionreturns at state 2012. At state 2010, function 1260 retrieves thediagnostic weight associated with the time profile from the givendisease's weight table and saves the weight with any other actualweights of the given disease. Moving to state 2012, function 1260returns to its caller function 1120 (FIG. 12).

[0454] Referring now to FIG. 21, the Update and Record function 160(FIG. 1) will be described. Prior to entry into Function 160, thediagnostic loop 100 has just recomputed the diagnostic weights of allcandidate diseases based on some new value for the current focussymptom. Function 160 now allows each candidate disease to take onesmall incremental diagnostic step, based on the new value. Function 160updates the diagnostic momentum, score, and status of each candidatedisease, and modifies and reviews their diagnostic ranking. If thislatest step causes one or more diseases to reach a diagnostic decisionpoint, function 160 processes that decision. Function 160 then preparesthe candidate disease list for another iteration of the diagnostic loop.

[0455] Function 160 begins with entry state 2102, where new weights havebeen established in all candidate diseases that use the current symptom.Moving to state 2104, function 160 initializes a loop to process eachdisease in the candidate disease list in turn, as disease D. Moving to adecision state 2106, if disease D does not use the current symptom, thenit cannot have been affected by the latest weight changes, so function160 skips the rest of the loop and moves to a decision state 2118. Butif, at decision state 2106, disease D does use the current symptom, thenfunction 160 moves to state 2108. At state 2108, function 160 sums allof the diagnostic weights added by the new symptom value D. Thisincludes the basic weight of the symptom value and all extra,incremental weights added by the various synergy functions. Moving tostate 2110, function 160 computes the diagnostic momentum of disease D,which, in one embodiment, is simply the sum computed at state 2108. Thismomentum is the incremental diagnostic progress made by disease D forthe current question. It is saved and used in another context toevaluate how fast disease D is advancing compared to other candidatediseases. Moving to state 2112, function 160 updates the diagnosticscore of disease D by adding to it the momentum computed in state 2110.

[0456] Moving to state 2114, function 160 reviews and adjusts thediagnostic status of disease D. Function 160 compares the new diagnosticscore to various author-defined thresholds that signal status changessuch as ruling disease D ‘in’ or ‘out’, or changing the diagnostic rankof disease D so that it will receive more or less attention in the nextiteration of the diagnostic loop. Moving to state 2116, function 160updates various working lists and databases to record the actions anddecisions taken with respect to disease D. Moving to decision state2118, if there are more candidate diseases to be processed, function 160moves to state 2120 which increments the loop index D and then movesback to state 2106 which starts another iteration. But if, at decisionstate 2118, there are no more diseases to be processed, function 160moves to state 2122 and returns to the caller, which in this case isfunction 170 of FIG. 1.

[0457] Referring now to FIG. 22, the Review Diagnoses function 170 asused in the diagnostic loop (FIG. 1) will be described. At the entry tofunction 170, the system has just updated all candidate diseases withnew diagnostic weights and scores, and function 170 now reviews thecandidate diseases to see if another iteration of the diagnostic loop isin order, or if diagnostic session goals or limits have been reached.Function 170 begins with entry state 2202, where all candidate diseaseshave just been updated. Moving to a decision state 2204, function 170reviews the diagnostic momentum and score of each candidate disease. Ifany one disease has advanced significantly enough to be selected as thenext current disease, function 170 moves to state 2208 to set thediagnostic mode to VAI for that disease, and then function 170 moves tofunction 2210. But if, at decision state 2204, no one disease has madespecial progress toward a diagnosis, function 170 moves to state 2206 toset the diagnostic mode to HAI, and then moves to function 2210.Function 2210 reviews the diagnostic goals and limits, which is furtherdescribed in conjunction with FIG. 23. At the completion of function2210, function 170 advances to state 2212 and checks to see if anyprocess in the loop has requested termination, adjournment or otherinterruption, or has modified the diagnostic mode or parameters oroptions used in the diagnostic loop 100. State 2212 also processesactions requests triggered by the action plateau feature, which allowsany disease object to request premature termination of the loop in favorof performing some other action such as is sometimes required inemergency cases. Moving to state 2214, function 170 sets internal flagsto either continue or terminate the diagnostic loop. Moving to state2216, function 170 returns to the caller, which in this case is state172 of FIG. 1.

[0458] Referring now to FIG. 23, the Check Goals and Limits function2210, as used in the diagnostic loop (FIG. 22), will be described. Infunction 2210, all candidate disease scores and diagnostic rankings havejust been updated, and the function must now review the candidatediseases to see if global diagnostic session goals or limits have beenreached. The diagnostic system is poised for another iteration of thediagnostic loop 100, and in function 2210 it determines whether anotheriteration is in fact required or advisable.

[0459] Function 2210 begins with entry state 2302, where all candidatediseases have just been updated. Moving to a decision state 2304, ifthere are no more candidate diseases to be diagnosed, function 2210moves to state 2324 to set the loop termination flag. But if, atdecision state 2304, there are more candidate diseases, function 2210moves to a decision state 2306. At decision state 2306, if thediagnostic goal is to rule some given number n of diseases in (or out),function 2210 moves to a decision state 2308; otherwise it moves to adecision state 2310. At decision state 2308, if at least n diseases haveindeed been ruled in (or out), function 2210 moves to state 2324 to setthe loop termination flag; otherwise function 2210 moves to decisionstate 2310. At decision state 2310, if the diagnostic goal is to rulecertain specified diseases in (or out), function 2210 moves to adecision state 2312; otherwise it moves to a decision state 2314. Atdecision state 2312, if the specified diseases have indeed been ruled in(or out), function 2210 moves to state 2324 to set the loop terminationflag; otherwise function 2210 moves to decision state 2314.

[0460] At decision state 2314, if the diagnostic loop is limited to somegiven time interval, function 2210 moves to a decision state 2316;otherwise it moves to a decision state 2318. At decision state 2316, ifthe specified time interval has elapsed, function 2210 moves to state2324 to set the loop termination flag; otherwise function 2210 moves todecision state 2318. At decision state 2318, if the diagnostic loop islimited to some given number of questions, function 2210 moves to adecision state 2320; otherwise it moves to state 2322. At decision state2320, if the specified number of questions have been asked, function2210 moves to state 2324 to set the loop termination flag; otherwisefunction 2210 moves to state 2322. At state 2322, function 2210 sets aninternal flag to continue the diagnostic loop and moves to return state2326. At state 2324, function 2210 sets an internal flag to terminatethe diagnostic loop and moves to state 2326 and returns to function 170(FIG. 22).

[0461] Referring now to FIG. 24, the Shut Down Diagnostic Loop function180 (FIG. 1) of the diagnostic loop 100 will be described. At the entryto function 180, the diagnostic loop is being terminated, and function180 now performs the actions that are part of an orderly termination andshut-down of the loop.

[0462] Function 180 begins with entry state 2402. Moving to state 2404,function 180 generates the required diagnostic reports. Moving to state2406, function 180 saves newly generated disease and symptom data.Moving to state 2408, function 180 saves the diagnostic loop “state” attermination, which enables the system to continue the loop at a futuretime by resetting all variables to the same “state”. Moving to state2410, function 180 releases all computer and system resources that wereallocated to the diagnostic loop 100. Moving to state 2412, function 180returns to state 182 of FIG. 1.

[0463] Referring now to FIG. 25, the Slope function 2500 that computesthe slope of two values with respect to time will be described. Theslope of an angle is its tangent, and the time slope of two values v1and v2 is (v2−v1)/(t2−t1). When t1=t2, this value is arithmeticallyundefined, and when t1 approaches t2, it will raise overflow conditionsin digital computers. This function uses an auxiliary result flag thatis suitably coded to inform the caller about any special problemsencountered during the calculation.

[0464] Function 2500 begins with entry state 2502, where the argumentst1, t2, v1, and v2 are assumed to be available to the function. Movingto state 2504, function 2500 examines data value v1. Proceeding to adecision state 2506, if no value v1 is given, function 2500 moves tostate 2508. At state 2508, function 2500 sets the result flag toindicate “no value v1” and returns at return state 2526. But if, atdecision state 2506, there is a value v1, function 2600 moves to state2510 and retrieves data value v2. Moving to a decision state 2512, if novalue v2 is given, function 2500 moves to state 2514, sets the resultflag to indicate “no value v2” and then returns at state 2526. But if,at decision state 2512, there is a value v2, function 2500 moves tostate 2516 and computes the tangent (v2−v1)/(t2−t1). Continuing to adecision state 2518, if the result of state 2516 raised an overflowerror condition, function 2500 moves to state 2520, sets the result flagto indicate “infinite slope” and returns at state 2526. But if, atdecision state 2518, the result did not overflow, function 2500 moves tostate 2522 and sets the result slope to the slope computed in state2516. Moving to state 2524, function 2500 sets the result flag toindicate “normal slope”. Moving to state 2526, function 2500 returns theresult slope and flag to the function (2600, FIG. 26 or 1510, FIG. 16)that called it.

[0465] Referring now to FIG. 26, the Trend function 2600, which computesthe trend of three points with respect to time, will be described. Ascomputed here, the time trend has three possible values: DECREASING,CONSTANT, and INCREASING, depending on whether the slope from point 2 topoint 3 is less than, equal to, or greater than the slope from point 1to point 2, respectively. For various values of t1, t2, and t3,computing these slopes may be arithmetically undefined or raise overflowconditions in digital computers; the function uses an auxiliary resultflag that is suitably coded to inform the caller about such specialconditions encountered during the calculation.

[0466] The Trend function 2600 begins with entry state 2602, where thearguments t1, t2, t3, v1, v2, and v3 are assumed to be available to thefunction. Moving to state 2604, function 2600 examines data value v1.Advancing to a decision state 2606, if no value v1 is given, function2600 moves to state 2608, sets the result flag to indicate “no value v1”and returns at return state 2644. But if, at decision state 2606, thereis a value v1, function 2600 moves to state 2610 and retrieves datavalue v2. Moving to a decision state 2612, if no value v2 is given,function 2600 moves to state 2614, sets the result flag to indicate “novalue v2” and returns at state 2644. But if, at decision state 2612,there is a value v2, function 2600 moves to state 2616 and retrievesdata value v3. Moving to a decision state 2618, if no value v3 is given,function 2600 moves to state 2620, sets the result flag to indicate “novalue v3” and returns at state 2644. But if, at decision state 2618,there is a value v3, function 2600 moves to execute function 2500 (FIG.25).

[0467] At function 2500, the slope from point 1 to point 2 is computedas (v2−v1)/(t2−t1). Moving to a decision state 2624, if the result ofexecuting function 2500 raised an overflow error condition, function2600 moves to state 2626. At state 2626, function 2600 sets the resultflag to indicate “infinite slope 1” and returns at state 2644. But if,at decision state 2624, the result did not overflow, function 2600 movesto execute function 2500′ (FIG. 25). At function 2500′, the slope frompoint 2 to point 3 is computed as (v3−v2)/(t3−t2). Moving to a decisionstate 2630, if the result of function 2500′ raised an overflow errorcondition, function 2600 moves to state 2632. At state 2632, function2600 sets the result flag to indicate “infinite slope 2” and returns atstate 2644. But if, at decision state 2630, the result did not overflow,function 2600 moves to a decision state 2634 and compares slope 1 toslope 2, using predefined comparison ranges. If, at decision state 2634,slope 1 is greater than slope 2, function 2600 moves to state 2636; ifslope 1 is less than slope 2, function 2600 moves to state 2640; ifslope 1 is equal to slope 2, function 2600 moves to state 2638. At state2636, function 2600 sets the result trend to indicate a decreasingtrend, and then moves to state 2642. At state 2638, function 2600 setsthe result trend to indicate a constant trend, and then moves to state2642. At state 2640, function 2600 sets the result trend to indicate anincreasing trend, and then moves to state 2642. At state 2642, function2600 sets the result flag to indicate a normal result. Moving to state2644, function 2600 returns the computed trend and the result flag tothe calling function 1520 (FIG. 17).

[0468] Referring to FIG. 27, a simple conceptual way of showing the useof actual and alternative symptom weights in arriving at a diagnosticscore will now be described. Although the actual implementation maydiffer, the diagram illustrates the relationships among the diseases,symptoms and weights. In one embodiment, a disease-symptom matrix isused where a plurality of diseases are listed in the columns and aplurality of related symptoms are listed in the rows. In a partialexemplary headache disease-symptom matrix 2700, the column 2702 listssymptoms in the rows marked as 2732 and at 2742. Several exemplarydiseases are shown at the columns marked as 2704 (common migraine), 2706(classic migraine), 2708 (cluster headache), and 2710 (subarachnoidhemorrhage). For each disease, there is a column for holding an “actual”value (columns 2714, 2716, 2718, and 2720) or an “alternative” value(columns 2715, 2717, 2719, 2721). As described above, a consultationbegins in HAI mode in the area marked 2730. Questions to elicitexemplary symptoms in the area marked 2732 are asked of the patient. Asquestions associated with the symptoms are asked of the patient, a valuefor the particular symptom is placed in either the actual column or thealternative column for each applicable disease as assigned by the authorof a particular disease. For example, a symptom “Nausea2” is defined bythe author of Common Migraine to be an “actual” symptom and has a valueof 35 as determined by the answer of the patient. However, the symptom“Nausea2” is defined by the author of Cluster Headache to be an“alternative” symptom and has a value of −20 as determined by the answerof the patient. Therefore, while in HAI mode, some diseases will haveactual symptoms elicited and some diseases will have alternativesymptoms elicited.

[0469] After one of several possible criteria is reached, the mode isswitched from HAI mode (2730) to VAI mode (2740) by the system, such asshown at 2734. From that point forward, the system concentrates onasking the symptoms for a focus disease based on reaching the criteria.In one embodiment, the “actual” symptoms for the focus disease (classicmigraine in this example) are then elicited as shown at 2742. Forexample, the criteria may include the fact that a particular diagnosticscore is reached or passed, a particular diagnostic momentum isachieved, a probability of diagnosis is achieved, or the user may ask toswitch modes. The user may request to have the actual symptoms for thefocus disease to be elicited, or even have the system go back and re-askonly the actual symptoms for the focus disease as shown at 2744. Otherweights (not shown), such as for various types of synergies, are addedin for the diseases in the area marked 2746. The exemplary diagnosticscores for each disease column are shown at row 2750 and a total scorefor each disease, which sums the actual and alternative scores for eachdisease, is shown in row 2752. A diagnosis may be declared for a diseasehaving a total score that meets or exceeds a particular threshold. Inthis example, the system diagnoses the patient as having classicmigraine, with a score of 480.

[0470] Referring now to FIG. 28, this figure depicts a form or screendisplay that lets a patient arrange a set of symptoms into the timeorder in which they actually occurred in the patient. This is oneembodiment that uses a graphic user interface and input form to obtainthe patient's input. Other embodiments use other techniques to obtainthe same information from the patient. In the diagnostic loop 100 (FIG.1), the system uses a valuator object (FIG. 6) to obtain the patient'sdisease timeline as a value. The valuator object then builds a timeprofile of the patient's symptoms, compares it to time profilespreviously stored in a database, and adds extra diagnostic weight todiseases that match the patient (FIG. 20). In another part of thediagnostic loop, the patient's disease timeline profile can be used, bywell-known pattern matching techniques, to filter the candidate diseaselist and thus reduce it to the most likely candidate diseases (FIG. 3).In another part of the diagnostic loop, the patient's disease timelinecan be used to identify the First Significant Symptom (FIG. 13). Inanother part of the diagnostic loop, the patient's disease timeline canbe used to select a disease that closely matches the patient timeprofile as the next focus disease (FIG. 4). Disease timelines,therefore, can be used to both reduce the set of candidate diseases aswell as to help diagnose a particular candidate disease.

[0471]FIG. 28 shows a chart 2800 that plots symptom onset and durationagainst time for four different symptoms. The time scale (line 2810) isshown in hours and runs from left to right, so that symptoms that appearearlier are placed more to the left than symptoms that occur later. Asan example, four symptoms (Anorexia, Nausea, Epigastric Pain, andRight-Lower-Quadrant Pain) are shown to depict the chronologicalsequence in which they typically appear in the disease Appendicitis.Other diseases may, of course, also exhibit this same time line or timeprofile of symptoms. The chart shows (line 2812) that classicAppendicitis typically begins with Anorexia (loss of appetite), which istherefore placed at the very left of the time line, to mark the originor start of the disease process in the patient. One hour after the onsetof Anorexia, a patient with Appendicitis will typically experienceNausea, which is therefore shown as starting at the 1-hour mark (line2814). About 2.5 hours into the disease, a patient will typicallyexperience Epigastric Pain (discomfort in the stomach area), and so thatsymptom is shown (line 2816) as starting between time 2 and 3 on thetime scale. Similarly, RLQ Pain (pain in the right lower quarter of theabdomen) is shown as beginning about 4 hours after the disease starts(line 2818). Assuming that the patient has previously indicated thepresence of the symptoms Anorexia, Nausea, Epigastric Pain, and RLQPain, the diagnostic system offers this type of chart to the patient.The patient then uses a mouse to move the symptom blocks left and rightuntil they are in a position that indicates when they appeared. Theblocks themselves can be stretched or shrunk to indicate how long theylasted. Then the patient submits the selections by clicking on a Submit(or similar) button 2820.

[0472] Referring now to FIG. 29a, an object embodiment 2900 of theentire MDATA system using techniques of object-oriented programming,i.e., as a collection of software objects that are capable of diagnosinga patient. In this embodiment 2900, all data is converted into “objects”by surrounding the data (now called “members”) with software proceduresand functions (now called “methods”) that manipulate and maintain thedata. Programs outside of the object are only permitted to access memberdata by using an object method or by special permission. Thisobject-oriented embodiment represents a rearrangement and redistributionof data and processes that has several benefits. First, theobject-oriented embodiment guarantees the validity of the object's data,data formats, and data structures at all times, regardless of theunpredictable dynamics of the surrounding diagnostic environment.Second, each object of a system can accumulate data, thus tracking bothits own processing history and that of its neighboring objects. It cantherefore assess its current status, compare itself to other objects,and acquire an awareness that it can use to make intelligent decisions.Third, given a memory, an awareness, and methods for acting on itsenvironment permits an object to be used as an agent that can actindependently to perform a task which is useful to the system as awhole.

[0473] The object-oriented embodiment is well known in the programmingindustry, but is used here in a novel and non-obvious manner to performautomated medical diagnosis. In other diagnostic systems, diseases andobjects are treated as inanimate data records that are manipulated by acentral program to compute a diagnosis. In the embodiment described inFIGS. 29a and 29 b, diseases and symptoms are designed as objects thatcan behave as intelligent actors that organize themselves at variouslogical levels, cooperate competitively for diagnosis, and ultimatelysort themselves into a differential diagnosis. For the purpose ofillustration, a software object is represented as a simple circle thatis assumed to encapsulate all of the object's data, and severalrectangles that represent object functions that belong to the object butcan be accessed by the outside world. Seen as such, a software objectcan be used as a “smart data record” that can act independently of otherobjects and can retain a memory of its actions for future reference.

[0474]FIG. 29a shows several such objects, as they would be designed toparticipate in the object embodiment 2900 of the MDATA system. Object2901 may represent a portion of the system, e.g., the list-based engine,whose actions are detailed in FIGS. 1 to 26. The functions performed bythe system in the object embodiment are shown grouped around the object.For example, the function INITIALIZE SELF would be called by theexternal system to cause the system to prepare for a diagnostic session,and the function REPORT STATUS would be called by any process requiringsome information about the current status of the system. Object 2902represents the Candidate Disease List; its functions perform servicesrelated to that list. For example, the function FORM CANDIDATE DISEASELIST would be called by the system object to begin a diagnostic sessionas shown in FIG. 2. Object 2903 is a Disease Object, which representsall of the medical knowledge the system has about a single disease. Itsfunctions provide services related to diseases, such as performing adiagnosis or updating itself with a new diagnostic score. Object 2904 isa Symptom Object, which represents the data and functions related to asingle symptom. For example, one of its functions is to GET VALUE ATTIME T, which activates the appropriate valuator functions needed toobtain a value, by computation, table lookup, or questioning of thepatient. Object 2905 is a Valuator Object, whose role is to perform thedetailed actions required to obtain a value. Object 2906 is a QuestionObject, which handles the tasks required to question a human patient.Object 2907 is a Node Object, whose role is to handle the actualinterface between the digital computer and the human patient. In oneembodiment, the node object is the only object in the entire MDATAsystem that actually communicates with the patient.

[0475] Referring now to FIG. 29b, an object-oriented embodiment of howthe MDATA system (FIG. 29a) might use a collection of objects to performa single iteration of the Diagnostic Loop (FIG. 1) will be abstractlydescribed. Instead of using a single program or engine that contains aset of central functions that perform operations on data (FIG. 1), theobject-oriented embodiment of FIG. 29b uses functions distributed as‘methods’ into various objects that either perform the functionsthemselves or delegate operations to still other objects. For example,whereas in FIG. 1, the system calls a function to select the next focussymptom, in FIG. 29b, it is the current disease object that selects thenext symptom.

[0476] In general, each object performs its own tasks and calls uponother objects to perform their tasks at the appropriate time. Over time,the sequence of operations creates a natural task hierarchy, from higherto lower levels of detail, using as many or as few levels as is requiredto perform the main task. At the same time, the hierarchy representslogical interpretations and meanings at different levels. Thus, at thelowest level, a patient is answering a single question; at the middlelevel, this is interpreted as a change in symptom information about thepatient; at the highest level, it may result in a reranking of thetentative diagnosis of several competing diseases.

[0477] The ability to capture and embody the complex interpretive andanalytical tasks of medical diagnosis into software is what gives theMDATA design a major advantage over other automated diagnostic systems,which tend to operate at a single level of action and meaning. What isgained by the object embodiment is the self-organizing andself-assessment ability that emerges from a system whose data areallowed to have the autonomy to organize themselves, based on certainglobally controlling principles.

[0478]FIG. 29b summarizes a process 2915 that may take place in anobject-oriented embodiment of the MDATA system in order to perform thesame processes that are detailed in FIGS. 1 through 26, with the addedcomplication that questions are posed to an on-line patient in thepatient's native language or other desired language, for example,French. The process 2915 starts when an external process has assembled aset of candidate diseases and now calls method 2921 to compute adifferential diagnosis based on the candidate diseases. Note that thefurther processing by the system occurs to output a particulardiagnosis. Method 2921 is one of many method functions of Engine Object2920. Engine Object 2920 encapsulates all data and processes of at leasta portion of the system and its numerous methods, of which only methods2921 and 2922 are shown here for illustration. The main purpose of theEngine Object is to accept external system and user requests andinitiate the appropriate internal processing. At the appropriate time,process 2915 advances to method 2931, which is a method of a DiseaseObject 2930. Disease Object 2930 encapsulates all data and processes ofa typical Disease Object. It has numerous methods; only methods 2931 and2932 are shown here for illustration. A Disease Object represents allthat is known about one given medical disease; its main function is toaccept requests for disease information and to carry out externalrequests for action. In the case being illustrated, the Disease Object2930 selects one of its Symptom Objects and moves to method 2941 toobtain the value of that symptom in the on-line patient. Symptom Object2940 contains all data and processes of a typical Symptom Object, withmany methods of which only methods 2941 and 2942 are shown here forillustration. A Symptom Object represents all that is known about onegiven symptom; its main function is to accept requests for symptominformation. The Symptom Object initiates internal processing to obtainactual symptom values and proceeds to method 2951.

[0479] Method 2951 is one method of a Question Valuator Object 2950.Valuator Object 2950 has numerous methods, but only methods 2951 and2952 are shown here. In general, a Valuator Object is responsible forperforming the computations required to compute the value of a symptomat some time t. A question valuator object initializes the processingrequired to select and ask a human questions and then advances to method2961. Method 2961 is a method of Question Object 2960, which representsthe members and methods of a typical Question Object and its manymethods, of which only methods 2961 and 2962 are shown here. A QuestionObject represents all of the data involved in asking a human a question,such as the natural language to be used or the education level of thepatient. The object's main function is to handle the stream of detailedquestions that are usually required to pose a question and elicit avalid response from a human. This question object selects the nodeobjects that are written to display questions in the patient's nativelanguage. Process 2915 then moves to method 2971 of Node Object 2970.Node Object 2970 encapsulates all data and processes of a typical nodeobject and its methods (only methods 2971 and 2972 are shown here). Thisnode object handles the physical details of a single question/responseexchange between a human and a computer, including special hardware,video, and audio problems, time delays, time-outs, and any need torepeat. The Node Object initiates the requisite processing, and thenmoves to method 2981. Method 2981 asks a question of a patient who maybe accessing the system over a data communications network, theInternet, for example. Assuming a GUI embodiment, the question will bedisplayed on a screen as a dialog, with appropriate buttons for aresponse from the patient via a patient object 2980.

[0480] When the patient responds at state 2982, a reverse sequence ofmethods begins as the process 2915 backs up the object hierarchy. Fromstate 2982, process 2915 moves to method 2972, where the patient'sresponse is noted and time-stamped. Proceeding to method 2962, theresponse is noted as the final response in a possible question stream.Moving to method 2952, the response is encoded for internal processing,so that it loses its native language aspect here. Moving to method 2942,the response is processed as a new symptom value at time t. Moving tomethod 2932, the response is processed as an increment in the disease'sdiagnostic score. Moving to method 2922, the response activates areranking of the relative disease positions in a differential diagnosisand then performs thresholding to determine a diagnosis. Finally, theengine object 2920 can now repeat the process or terminate, depending onthe external option settings. Other functions and objects associatedwith the LBE which are expected before, during or after this controlflow may be included in various embodiments. For instance, once adisease ranking is computed, thresholding may occur to determine adiagnosis of one disease. Furthermore, actions may occur such asupdating a patient's electronic medical record or notifying a doctor orother healthcare practitioner of a diagnostic event, e.g., a situationrequiring immediate treatment.

[0481] Referring now to FIG. 30, a process 3000 describes howalternative symptoms are established in an off-line preparation mode andlater used to diagnose a patient in an on-line mode. The off-line andon-line modes are shown here as if they occur in sequence; but inpractice they are typically separated by additional preparation stepssuch as testing, approval, auditing, and final integration into aproduction database.

[0482] Process 3000 begins at state 3002, where one or more diseaseobjects are to be created by a medical author. Moving to state 3004, onedisease object D is established by defining its member functions anddata. One major data structure of disease D is the list of symptoms thatcharacterize the disease. Each symptom S of that list must be definedand described. Moving to state 3006, one symptom S of the list isestablished and defined in terms of its values and their diagnosticweights. Moving to a decision state 3010, if the symptom object canperhaps be used as alternative symptoms in other disease objects, thediagram moves to state 3012; otherwise it moves to a decision state3014. At state 3012, the symptom is established as an alternativesymptom in the applicable disease objects. Moving to decision state3014, if there are more symptom objects to be established for disease D,the diagram moves back to state 3006; otherwise it moves to a decisionstate 3016. At decision state 3016, if there are more disease objects tobe established, the diagram moves back to state 3004; otherwise itterminates the off-line phase.

[0483] For the on-line phase, process 3000 describes how alternativesymptoms are processed inside the diagnostic loop 100 during anautomated diagnostic session with a patient (or a patient's agent) whois on-line and able to respond to questions posed by the system (FIG.1). Moving to state 3030, a disease D is selected as the focus fordiagnosis (FIG. 4). Moving to state 3032, one symptom object S ofdisease D is selected as the focus for diagnosis (FIG. 5). Symptom Smust now be evaluated, which may be a complex, time-consuming process(FIG. 6). Moving to a decision state 3034, process 3000 shows a portionof the evaluation process that deals with the use of alternativesymptoms, which are used to save on-line time. It may be the case that,for disease D, symptom S has an acceptable alternative symptom that hasalready been evaluated. If so, the diagram bypasses evaluation ofsymptom S and instead moves to state 3036. But if, at decision state3034, there is no alternative symptom, process 3000 moves to state 3038for evaluation of symptom S. At state 3036, the weights of thealternative to symptom S are retrieved and applied to the diagnosticscore of disease D, and then process 3000 moves to a decision state3040. However, at state 3038, symptom S is evaluated (FIG. 6), thediagnostic weights of symptom S are retrieved and applied to thediagnostic score of disease D (FIGS. 11 and 21), and then process 3000moves to state 3040. At state 3040, if some terminating condition isreached for disease D (FIG. 22), process 3000 moves to a decision state3044; else it moves to state 3032. At decision state 3044, if there areother disease objects to process, process 3000 moves back to state 3030;otherwise it moves to the end state 3046.

[0484] Referring now to FIG. 31, a process 3100 describes how symptomobjects can be re-used as alternative symptoms in a disease object. TheAlternative Symptoms feature allows the diagnostic system to substitutespecified symptom values for others in order to bypass time-consumingevaluation of a given symptom when acceptable alternative symptom valuesare already available. The feature accommodates the individualpreferences of medical authors, simplifies the processing of symptomsstored in various equivalent formats, and allows the sequencing ofsymptom evaluation to be more adaptive to the dynamics of an on-linediagnostic session, instead of depending on prescribed sequence ofsymptom evaluation.

[0485] Process 3100 only shows the general steps of the off-linepreparation of one disease object. How the disease is diagnosed on-lineis shown in FIG. 1, and how alternative symptoms are used is describedin conjunction with FIGS. 6 and 30. Process 3100 begins at state 3102,where an author wants to create and describe a disease object D. Movingto state 3104, the disease object D is established and its memberfunctions and data are defined. One major data structure of disease D isthe list of symptoms, symptom values, and symptom timelines thatcharacterize the disease. Each symptom of that list is identified anddescribed. Moving to state 3110, a database of all existing symptomobjects is accessed for the purpose of locating possible alternativesymptoms. Advancing to state 3112, one symptom S of disease D's symptomlist is selected. Proceeding to state 3114, the symptom database issearched, and some symptom A is identified as an acceptable alternativeto symptom S for diagnosing the disease D. Moving to state 3116,diagnostic weights are assigned to the values of alternative symptom A.Moving to a decision state 3118, if more symptoms of disease D are to beprocessed, process 3100 moves back to state 3112; otherwise it moves toend state 3122.

[0486] Referring now to FIG. 32a, a process 3200 used by an author toestablish disease and symptom objects or elements will be described.Note that the diagram shows processing that is performed off-line, atdata preparation and testing time, and not on-line at diagnostic time.

[0487] Process 3200 begins at state 3202, where the author wants todefine one or more disease objects and their symptom objects. Moving tostate 3204, multiple disease objects are established, each with therequisite disease object data and disease object processing functions.Proceeding to state 3206, multiple symptom objects are identified foreach disease object. Symptoms that already exist in a symptom databaseare identified. New symptoms are described, including a list of theirpossible values. Advancing to state 3208, a diagnostic weight isassigned to some or all values of each symptom object. Each symptomobject typically has many possible values, and each value may have anassigned diagnostic weight toward the diagnosis of its associateddisease. Moving to state 3212, process 3200 ends the off-line portion atstate 3212.

[0488] Referring to FIG. 32b, one embodiment of the use of specifiedsymptom elements or objects for disease elements or objects will now bedescribed by on-line process 3230. The off-line portion (process 3200)was previously described in conjunction with FIG. 32a. Process 3230illustrates one embodiment of the HAI and VAM concepts along with theuse of specified symptom elements.

[0489] Process 3230 begins at an enter state 3232 in the HAI modepreviously described. Process 3230 advances to state 3234 where aselected symptom element is evaluated, such as performed by the ObtainSymptom Value function 140 (FIG. 6). Proceeding to state 3236, process3230 applies the diagnostic weight of the symptom value to thediagnostic score of disease elements for which the symptom element isapplicable. Moving to a decision state 3238, process 3230 determines ifthe criteria for switching to the evaluation of specified symptomelements (i.e., in the VAI mode) for one of the disease elements (theNth disease element) is reached. Examples of criteria are a highdiagnostic momentum, a high diagnostic score, a high diagnosticprobability, some external process requested the switch or a humanrequested the switch to the use of specified symptoms. If the criteriais not reached at decision state 3238, process 3230 proceeds to state3240 to continue evaluation of symptom elements where the specifiedsymptom elements for any disease element are not necessarily selected.Process 3230 moves back to state 3234 to loop through further symptomelements until the criteria at decision state 3238 is reached.

[0490] If the criteria is reached at decision state 3238, process 3230proceeds in the VAI mode to state 3242 where a specified symptom elementfor the Nth disease element is evaluated. Continuing at state 3244,process 3230 applies the weight of the evaluated symptom element to thediagnostic score of the Nth disease (and any other disease for which itis a specified symptom element) and alternative symptom weight(s) to thediagnostic score(s) of other appropriate disease element(s). Advancingto a decision state 3246, process 3230 determines if the diagnosticscore for any disease element has reached or passed a threshold fordiagnosis. If so, the diagnostic scores for the relevant diseases (thosebeing currently evaluated) are returned at end state 3248. If thediagnostic score for any disease element has not reached or passed thethreshold, as determined at decision state 3246, process 3230 proceedsto a decision state 3250 to determine if further specified symptomelements are available to be evaluated for the Nth disease element. Ifso, process 3230 continues at state 3252 to continue evaluation ofsymptom elements where the specified symptom elements for the Nthdisease element are selected. Process 3230 moves back to state 3242 toloop through specified symptom elements of the Nth disease until thereare no further specified symptom elements available, as determined atdecision state 3250. When decision state 3250 determines there are nofurther specified symptom elements available, process 3230 advances tostate 3254 to check the diagnostic score for each of the remainingdisease that are currently being evaluated. Process 3230 then moves backto decision state 3238 to determine if the VAI mode should beestablished any other disease element or if further processing shouldcontinue in HAI mode, as previously described.

[0491] Referring now to FIG. 33, a process 3300 summarizes the majorsteps of a fully automated method that uses the novel concept of diseasetimeline matching to diagnose a patient's medical condition. A diseasetimeline is a data structure that records the symptoms of a specificdisease in chronological order and describes the key symptomcharacteristics such as onset, duration, magnitude, and offset of eachsymptom. Disease timelines can be generic or actual. Generic timelinesdescribe the disease statistically, as it evolves in a typicalpopulation; actual timelines describe the symptoms of a specific patientbeing diagnosed. Timelines are abstract data structures that can berepresented in various ways such as graphs, charts, calendars, lists,tables, spreadsheets, or software objects. A simple example is anhour-by-hour description of a disease process in the form of a GanttChart, as shown in FIG. 28.

[0492] The diagnostic process 3300 outlined in FIG. 33 exploits the factthat it is possible to take an actual disease timeline, use well-knownmathematical techniques to match it to a database of generic diseasetimelines, and thus identify a relatively small set of diseases that arestrong candidates for further analysis and ultimate diagnostic ranking.As outlined in FIG. 33, the process has two separate phases. In thefirst phase (states 3302 to 3304), a computer program assists physicianauthors as they prepare a database of generic disease timelines. In thesecond phase (states 3306 to 3320), another program uses various symptomcharacteristics to match the timeline of an on-line patient to thegeneric timelines in the database and to appropriately increment thediagnostic score of matching diseases.

[0493] State 3302 is the start of the process 3300. Moving to state3304, using an off-line program, medical authors generate a database ofgeneric disease timelines. This state may represent several years ofprofessional labor to generate a massive database of thousands ofdiseases and their symptoms. It may also include further extensive laborto format and test the data and to prepare it for use in a fullyautomated diagnostic system such as described in states 3306 through3320.

[0494] Proceeding to state 3306, a patient is on-line to a computerprogram which elicits a chief complaint from a patient by askingquestions. Moving to state 3308, the program receives answers from thepatient. Advancing to state 3310, the program uses the responses toidentify one or more diseases that correspond to the chief complaint.Moving to state 3312, the program correlates the chief complaint to atimeline for all identified diseases.

[0495] Continuing at state 3314, the program asks the patient questionsto determine the patient's FSS time parameters and locates this on thedisease timeline. Moving to state 3316, the program adds a predeterminedincremental diagnostic weight to the diagnostic score of all identifieddiseases if the patient's FSS matches the disease FSS. Proceeding tostate 3318, the program establishes a diagnosis for one or more of theidentified diseases when its cumulative diagnostic score reaches orexceeds a threshold. The process 3300 ends at state 3320.

[0496] Referring now to FIG. 34, a filly automated process 3400 thatdiagnoses a patient's medical condition using symptom magnitudepatterns, which identify the changes in magnitudes of each symptoms of aspecific disease in chronological order, will be described. A magnitudepattern is, in effect, a disease timeline with magnitudes of thesymptoms, creating a disease profile. Like a disease timeline, a symptommagnitude pattern can be can be generic or actual, i.e., typical orpatient-specific.

[0497] Process 3400 begins at state 3402. Moving to state 3404, using anoff-line program, medical authors generate a database of generic symptommagnitude patterns, analogous to the disease timelines described forFIG. 33. Proceeding to state 3406, a computer program asks an on-linepatient questions to elicit the magnitude of the patient's symptom.

[0498] Continuing at state 3408, the program develops a pattern orprofile of the patient's symptoms and their magnitudes. Moving to state3410, the program compares the patient's symptom magnitude pattern toits database of symptom magnitude patterns, to (attempt to) identify thepatient's disease. Process 3400 ends at state 3412.

[0499] Referring to FIG. 35, a block diagram of one embodiment of theMDATA system 3500 will be described. The MDATA system 3500 includes anetwork cloud 3502, which may represent a local area network (LAN), awide area network (WAN), the Internet, or another network capable ofhandling data communication.

[0500] In one embodiment, the MDATA programs and databases may reside ona group of servers 3508 that may be interconnected by a LAN 3506 and agateway 3504 to the network 3502. Alternatively, in another embodiment,the MDATA programs and databases reside on a single server 3510 thatutilizes network interface hardware and software 3512. The MDATA servers3508/3510 store the disease/symptom/question lists or objects describedabove.

[0501] The network 3502 may connect to a user computer 3516, forexample, by use of a modem or by use of a network interface card. A user3514 at computer 3516 may utilize a browser 3520 to remotely access theMDATA programs using a keyboard and/or pointing device and a visualdisplay, such as monitor 3518. Alternatively, the browser 3520 is notutilized when the MDATA programs are executed in a local mode oncomputer 3516. A video camera 3522 may be optionally connected to thecomputer 3516 to provide visual input, such as visual symptoms.

[0502] Various other devices may be used to communicate with the MDATAservers 3508/3510. If the servers are equipped with voice recognition orDTMF hardware, the user can communicate with the MDATA program by use ofa telephone 3524. A telecommunications embodiment, e.g., using atelephone, is described in Applicant's U.S. patent entitled“Computerized Medical Diagnostic and Treatment Advice System,” U.S. Pat.No. 5,660,176, which is hereby incorporated by reference. Otherconnection devices for communicating with the MDATA servers 3508/3510include a portable personal computer 3526 with a modem or wirelessconnection interface, a cable interface device 3528 connected to avisual display 3530, or a satellite dish 3532 connected to a satellitereceiver 3534 and a television 3536. Other ways of allowingcommunication between the user 3514 and the automated diagnostic system3508/3510 are envisioned.

VI. CONCLUSION

[0503] Specific blocks, sections, devices, functions and modules mayhave been set forth. However, a skilled technologist will realize thatthere are many ways to partition the system of the present invention,and that there are many parts, components, modules or functions that maybe substituted for those listed above.

[0504] As should be appreciated by a skilled technologist, the processesthat are undergone by the by the above described software may bearbitrarily redistributed to other modules, or combined together in asingle module, or made available in a shareable dynamic link library.The software may be written in any programming language such as C, C++,BASIC, Pascal, Java, and FORTRAN and executed under a well-knownoperating system, such as variants of Windows, Macintosh, Unix, Linux,VxWorks, or other operating system. C, C++, BASIC, Pascal, Java, andFORTRAN are industry standard programming languages for which manycommercial compilers can be used to create executable code.

[0505] While the above detailed description has shown, described, andpointed out the fundamental novel features of the invention as appliedto various embodiments, it will be understood that various omissions andsubstitutions and changes in the form and details of the systemillustrated may be made by those skilled in the art, without departingfrom the intent of the invention.

What is claimed is:
 1. A data schema for diagnosing a disease,comprising: a first disease object associated with a set of firstdisease symptom objects, at least one first disease symptom objecthaving an actual symptom weight; and a second disease object associatedwith a set of second disease symptom objects, at least one seconddisease symptom object corresponding to the at least one first diseasesymptom object and having an alternative symptom weight.
 2. A method ofautomated medical diagnosis of a patient, comprising: providing at leasta first symptom element having a first symptom weight; retrieving analternative weight for the first symptom; and applying the retrievedalternative weight to a diagnostic score so as to diagnose a medicalcondition.